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As a social information product, the privacy and usability of high-dimensional data are the core issues in the field of privacy protection. Feature selection is a commonly used dimensionality reduction processing technique for high-dimensional data. Some feature selection methods only process some of the features selected by the algorithm and do not take into account the information associated with the selected features, resulting in the usability of the final experimental results not being high. This paper proposes a hybrid method based on feature selection and a cluster analysis to solve the data utility and privacy problems of high-dimensional data in the actual publishing process. The proposed method is divided into three stages: (1) screening features; (2) analyzing the clustering of features; and (3) adaptive noise. This paper uses the Wisconsin Breast Cancer Diagnostic (WDBC) database from UCI's Machine Learning Library. Using classification accuracy to evaluate the performance of the proposed method, the experiments show that the original data are processed by the algorithm in this paper while protecting the sensitive data information while retaining the contribution of the data to the diagnostic results. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for the early detection and diagnosis of breast cancer using the Wisconsin dataset. The methodology involves four main steps, including data collection, preprocessing, feature selection, and classification. Fine needle aspiration technique is used to extract the ultrasound image features of breast cancer, and preprocessing is performed to eliminate outliers, null values, and noise. Redundant parameters are removed during the feature selection process to improve accuracy. Six machine learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Random Forest, Decision Tree, and Gaussian Naive Bayes, are employed for the classification of the breast cancer dataset. Support Vector Machine and K-Nearest Neighbor achieved the highest accuracy, with Logistic Regression, Gaussian Naive Bayes, Random Forest, and Decision Tree having lower accuracy scores. The proposed methodology could aid in the timely detection and diagnosis of breast cancer, and help doctors in selecting the optimal clinical treatment plan for their patients. Further work will be carried out to investigate the effectiveness of additional preprocessing algorithms in improving the classification accuracy of the breast cancer dataset. [ABSTRACT FROM AUTHOR]

Breast cancer is a cancer disease that seriously threatens women's health and occupies the first place in female cancer mortality. At present, the incidence rate of breast cancer in China is the first in the world and is on the rise. In view of the serious harm of breast cancer to life and health, researchers and institutions are making unremitting efforts to find a perfect diagnosis and treatment plan. With the improvement of computer performance and machine learning levels, intelligent algorithms have been able to replace human behavior and judgment in some fields. The traditional breast cancer diagnosis process requires medical experts to observe patient data repeatedly. In this case, the algorithm technology is used to quickly feedback a high probability reference result to doctors, which is particularly important to increase the diagnosis efficiency and reduce the burden of doctors. In order to improve the accuracy of existing breast cancer recognition methods, this paper proposes and implements a scheme based on a whale optimization algorithm to iteratively adjust the key parameters of the support vector machine to improve the accuracy of breast cancer recognition. In order to verify the performance of the WOA-SVM algorithm, this paper uses the Wisconsin breast cancer data in the UCI database for performance verification experiments. Experiments show that the WOA-SVM model has higher recognition accuracy than the traditional breast cancer recognition model. [ABSTRACT FROM AUTHOR]

In recent decades, breast cancer has become one of the leading causes of mortality among women. This disease is not preventable because of its unknown causes; however, its early diagnosis increases patients' recovery chances. Machine learning (ML) can be utilized to improve treatment outcomes in healthcare operations while diminishing costs and time. In this research, we suggest two novel feature selection (FS) methods based upon an imperialist competitive algorithm (ICA) and a bat algorithm (BA) and their combination with ML algorithms. This study aims to enhance diagnostic models' efficiency and present a comprehensive analysis to help clinical physicians make more precise and reliable decisions. K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), Naive Bayes, AdaBoost (AB), linear discriminant analysis (LDA), random forest (RF), logistic regression (LR), and artificial neural network (ANN) are some of the methods employed. Sensitivity, accuracy, precision, mean absolute error F-score, root mean square error, Kappa, and relative absolute error calculated the performance of the methods. This paper applied a distinctive integration of evaluation measures and ML algorithms using the wrapper feature selection based on ICA (WFSIC) and BA (WFSB) separately. We compared two proposed approaches for the performance of the classifiers. Also, we compared our best diagnostic model with previous works reported in the literature survey. Experimentations were performed on the Wisconsin diagnostic breast cancer (WDBC) dataset. Results reveal that the proposed framework that uses the BA with an accuracy of 99.12% surpasses the framework using the ICA and most previous works. Additionally, the RF classifier in the approach of FS based on BA emerges as the best model and outperforms others regarding its criteria. Besides, the results illustrate the role of our techniques in reducing the dataset dimensions up to 90% and increasing the performance of diagnostic models by over 99%. Moreover, the result demonstrates that there are more critical features than the optimum dataset obtained by proposed FS approaches that have been selected by most ML models, including the standard error of area, concavity, smoothness, perimeter, the worst of texture, compactness, radius, symmetry, smoothness, concavity, and the mean of concave points, fractal dimension, compactness, concavity that can remarkably affect the efficiency of breast cancer prediction. This study illustrates the role of our approaches in enhancing treatment outcomes in healthcare operations. [ABSTRACT FROM AUTHOR]

The traditional negative selection algorithm with a randomly generated hypersphere detector is unable to satisfy the development needs of continuous learning due to the inherent defects of the detector. This paper proposes a novel negative selection algorithm for hyper-rectangle detectors that overcomes the shortcomings of randomly generated hyper-sphere detectors and lays the foundation for a negative selection algorithm with continuous learning capability. It uses self-sample clusters of equal-sized hypercubes instead of self-samples for training. The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions. [ABSTRACT FROM AUTHOR]

Recent scholarship in philosophy of science and technology has shown that scientific and technological decision making are laden with values, including values of a social, political, and/or ethical character. This paper examines the role of value judgments in the design of machine-learning (ML) systems generally and in recidivism-prediction algorithms specifically. Drawing on work on inductive and epistemic risk, the paper argues that ML systems are value laden in ways similar to human decision making, because the development and design of ML systems requires human decisions that involve tradeoffs that reflect values. In many cases, these decisions have significant—and, in some cases, disparate—downstream impacts on human lives. After examining an influential court decision regarding the use of proprietary recidivism-prediction algorithms in criminal sentencing, Wisconsin v. Loomis, the paper provides three recommendations for the use of ML in penal systems. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this paper, machine learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for breast cancer prediction. The Wisconsin diagnostic breast cancer (WDBC) dataset is used to train and test the model. Classification algorithms, including support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes, were employed. Performance measures for each of them were obtained, namely: accuracy, precision, recall, F-score, Kappa, Matthews correlation coefficient (MCC), and time. The results indicate that without feature selection, LightGBM achieves the highest accuracy at 95%. With minimum redundancy maximum relevance (mRMR) feature selection (15 features), LightGBM outperforms other classifiers, achieving an accuracy of 98%. For Pearson correlation coefficient feature selection (15 features), LightGBM also excels with a 95% accuracy rate. Lasso feature selection (5 features) produces varied results across classifiers, with logistic regression achieving the highest accuracy at 96%. These findings underscore the importance of feature selection in refining model performance and in improving detection for breast cancer. [ABSTRACT FROM AUTHOR]

As one of the most common types of cancer among women, breast cancer is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. However, detecting breast cancer is challenging due to imbalanced classification, where the minority class (cancerous) is ominously smaller than the majority class (non-cancerous). In this paper, we explore the use of logistic regression (LR) and the adaptive synthetic resampling (ADASYN) technique to address imbalanced classification in breast cancer detection. To that end, we collected the Wisconsin Breast Cancer dataset, which contains 569 instances. The dataset is imbalanced, with 212 malignant (cancerous) cases and 357 benign (non-cancerous) cases. Then, we trained support vector machine, LR, K-nearest neighbor, gradient, and adaptive boosting on the imbalanced dataset. Finally, we trained these algorithms on resampled data with the ADASYN oversampling and we evaluated their performance using cross-validation score with 5-folds. The results of the experiment showed that using ADASYN with LR significantly improved the performance the LR model. The LR model achieves 99.46% accuracy on breast cancer diagnosis. Moreover, the confusion matrix shows that among the 188 samples, the model misclassified one cancerous instance. Thus, we concluded that the proposed model is effective for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common cancers among females. Patients with breast cancer are regularly rising. The survival of patients can be improved by early diagnosis and treatment. Because of its success, machine learning is commonly used in most fields. In this paper, numerous methods for early detection of this disease are employed for machine learning. Here, we consider C 5.0, Naive Bayes, logistic regression, random forest, ctree, KNN, K-Mean, GBM, adaBoost, decision tree model to classify the breast cancer tumor and evaluate their performances based on Wisconsin and SEER datasets. The demonstrations of the classifiers were assessed using accuracy, precision, recall, and F1 measure. We also predict whether the tumor is dead or alive, considering the tumor size, the various cancer stages, and months' survival. The general research will increase people's understanding of breast cancer and reduce tumor fears. [ABSTRACT FROM AUTHOR]

Recently, a system of spintronic vortex oscillators has been experimentally trained to classify vowel sounds. In this paper, we have carried out a combination of device-level and system-level simulations to train a system of spin Hall nano oscillators (SHNOs) of smaller size (25X lower in area compared to those vortex oscillators) for such data classification tasks. Magnetic moments precess in an uniform mode as opposed to the vortex mode in our oscillators. We have trained our system to classify inputs in various popular machine learning data sets like Fisher's Iris data set of flowers, Wisconsin Breast Cancer (WBC) data set, and MNIST data set of handwritten digits. We have employed a new technique for input dimensionality reduction here so that the clustering/target synchronization pattern changes based on the nature of the data in the different data sets. Our demonstration of learning in a system of such small SHNOs for a wide range of data sets is promising for scaling up the oscillator-based neuromorphic system for complex data classification tasks. [ABSTRACT FROM AUTHOR]

Introduction: Advances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. Machine learning approaches cannot replace professional humans, but they can change the treatment of diseases such as cancer and be used as medical assistants. Background: Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. Feature selection and classification are common data mining techniques in machine learning that can provide breast cancer diagnosis with high speed, low cost and high precision. Methodology: This paper proposes a new intelligent approach using an integrated filter-evolutionary search-based feature selection and an optimized ensemble classifier for breast cancer diagnosis. The selected features mainly relate to the viable solution as the selected features are successfully used in the breast cancer disease classification process. The proposed feature selection method selects the most informative features from the original feature set by integrating adaptive thresholder information gain-based feature selection and evolutionary gravity-search-based feature selection. Meanwhile, classification model is done by proposing a new intelligent multi-layer perceptron neural network-based ensemble classifier. Results: The simulation results show that the proposed method provides better performance compared to the state-of-the-art algorithms in terms of various criteria such as accuracy, sensitivity and specificity. Specifically, the proposed method achieves an average accuracy of 99.42% on WBCD, WDBC and WPBC datasets from Wisconsin database with only 56.7% of features. Conclusion: Systems based on intelligent medical assistants configured with machine learning approaches are an important step toward helping doctors to detect breast cancer early. [ABSTRACT FROM AUTHOR]

This paper presents a new training algorithm that can update the situation of layers' network, and therefore, connections, neurons, and firing rate of neurons based on FORCE (first-order reduced and controlled error) training algorithm. The Quantized Full FORCE algorithm (QFF) also updates the number of neurons and connections between different layers in the network per iteration in a way that the whole firing rate of each layer is updated via selecting the best neurons and combining strong features. The update method is sequential, so that with each instance passing through the network, the network structure is updated with the Full FORCE algorithm. The algorithm updates the structure of networks with a multiple/single middle layer of the supervised version of feed forward networks such as Multilayer perceptron (MLP), changing them into partially-connected networks. A combination of principal component analysis PCA and Linear Discriminant Analysis (LDA) algorithms has been used to cluster the network input features. The paper focuses on the deep supervised MLP network with backpropagation (BP) and various datasets and its comparison with other MLP based stat of art methods and hybrid evolutionary algorithms. We achieved 98.15 percent accuracy for facial expression 98.6 and 97.7 percent for Wisconsin breast Cancer and Iris Flower in respectively. The training algorithm employed in the study enjoys a lower computational complexity while yielding faster and more accurate convergence, starting with a very low level of errors of 0.009 in comparison with the full connection network and it solves the challenge of getting stuck in local minima and poor convergence of Gradient Decent with BP. • Providing the new training algorithm to update the connections and firing rate of neurons in each iteration. • The proposed training model is based on Quantized Full FORCE (first-order reduced and controlled error) training. • Providing an accurate framework to update the number of neurons, converting the network to a partial connection network. • In the new training model, the best features have the highest number of connections and neurons within the middle layers. • Being the decision speed of the network in the proposed method much faster than a fully connected network. • Cost-effectiveness and much lower resource requirements for implementation of deep networks on hardware such as FPGAs. [ABSTRACT FROM AUTHOR]

Breast cancer was the most diagnosed form of cancer in 2020. Early diagnosis of breast cancer results in a significant improvement in long-term survival rates. Current methods require consultation with experts, which is expensive and time-consuming and thus may not be accessible to all. This paper seeks to train and evaluate supervised machine learning models for the accurate and efficient detection of breast cancer. The Wisconsin Breast Cancer Database dataset describes 30 attributes of cell nuclei, including, but not limited to, their radius, texture, and concavity. It contains 569 instances, 212 of which are malignant tumors. The Random Forest algorithm outperforms other algorithms in classifying breast tumors as either malignant or benign and is thus selected as our primary model. It is trained on two different subsets of the dataset having 16 and 8 features, respectively, identified with the help of multiple feature selection methods. The Random Forest models are tested post hyperparameter tuning on a holdout set, and accuracies of 100% and 99.30% respectively. The models are also compared with four other machine learning classification algorithms: Support Vector Machine (SVM), Decision Tree, Multilayer Perceptron, and K-Nearest Neighbors. The results confirm that Random Forest is the superior method for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Twitter integrates with streaming data technologies and machine learning to add new value to healthcare. This paper presented a real-time system to predict breast cancer based on streaming patient's health data from Twitter. The proposed system consists of two major components: developing an offline building model and an online prediction pipeline. For the first component, we made a correlation between the features to determine the correlation between features and reduce the number of features from the Breast Cancer Wisconsin Diagnostic dataset. Two feature selection algorithms are recursive feature elimination and univariate feature selection algorithms which are applied to features after correlation to select the essential features. Four decision trees, logistic regression, support vector machine, and random forest classifier have been used on features after correlation and feature selection. Also, hyperparameter tuning and cross-validation have been applied with machine learning to optimize models and enhance accuracy. Apache Spark, Apache Kafka, and Twitter Streaming API are used to develop the second component. The best model with the highest accuracy obtained from the first component predicts breast cancer in real time from tweets' streaming. The results showed that the best model is the random forest classifier which achieved the best accuracy. [ABSTRACT FROM AUTHOR]

This paper develops local learning algorithms to solve a classification task with the help of biologically inspired mathematical models of spiking neural networks involving the mechanism of spike‐timing‐dependent plasticity (STDP). The advantages of the models are their simplicity and, hence, the potential ability to be hardware‐implemented in low‐energy‐consuming biomorphic computing devices. The methods developed are based on two key effects observed in neurons with STDP: mean firing rate stabilization and memorizing repeating spike patterns. As the result, two algorithms to solve a classification task with a spiking neural network are proposed: the first based on rate encoding of the input data and the second based on temporal encoding. The accuracy of the algorithms is tested on the benchmark classification tasks of Fisher's Iris and Wisconsin breast cancer, with several combinations of input data normalization and preprocessing. The respective accuracies are 99% and 94% by F1‐score. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel classification approach based on Extreme Learning Machine (ELM) and Wavelet Neural Networks. We introduce two novel contributions. The first is Extreme Learning Machine based on Fast Wavelet Transform (ELM-FWT) algorithm aiming to solve the problem of matrix inversion which represents several limitations to ELM method. The second contribution is dedicated to solving another problem of machine learning algorithms, i.e. the number of neurons in the hidden nodes. It consists in allocating automatically and efficiently the number of neurons in the hidden layer. To demonstrate the effectiveness of our proposal, we realized numerous experiments and comparisons using 19 algorithms from Breast Cancer Wisconsin database which are considered as references in machine learning. Experimental results clearly demonstrate the stability and robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

In data mining, preprocessing is one of the essential processes which involves data normalization, noise removal, handling missing values, etc. This paper focuses on handling missing values using unsupervised machine learning techniques. Soft computation approaches are combined with the clustering techniques to form a novel method to handle the missing values, which help us to overcome the problems of inconsistency. Rough K-means centroid-based imputation method is proposed and compared with K-means centroid-based imputation method, fuzzy C-means centroid-based imputation method, K-means parameter-based imputation method, fuzzy C-means parameter-based imputation method, and rough K-means parameter-based imputation methods. The experimental analysis is carried out on four benchmark datasets, viz. Dermatology, Pima, Wisconsin, and Yeast datasets, which have taken from UCI data repository. The proposed method proves the efficacy of different datasets, and the results are also promising one. [ABSTRACT FROM AUTHOR]

Cancer is the principle wellspring of death around the globe with 2.09 million cases so far in 2018 [1]. Around 627000 deaths accounting to 6.6% are caused because of female breast cancer and it ranks five amongst the list of top causes for deaths, the prime reason being prognosis being favorable in developed countries. The timely empathy of breast cancer further makes the process of prognosis better hence improving the rates of survival, because this will indorse on time treatment which is given clinically to patients. When the classification is done in an accurate way for malignant and benign tumours, it stops the suffering of patients with excessive ailments. The best possible recognizable proof of breast cancer disease and the process of characterizing into benign and malignant groups is that the main concern of a ton of investigation and research. When thrown light on its particular advantages in significant alternatives recognition from the datasets of entangled breast cancer, the generally perceived option is Machine Learning, because of the philosophy of determination in breast cancer to arrange pattern and forecast modelling. This paper will in general, survey machine learning and assessment of this particular paper, WBCD: Wisconsin Breast Cancer Database has been used as the benchmark dataset. [ABSTRACT FROM AUTHOR]

Background: In classification and diagnostic testing, the receiver-operator characteristic (ROC) plot and the area under the ROC curve (AUC) describe how an adjustable threshold causes changes in two types of error: false positives and false negatives. Only part of the ROC curve and AUC are informative however when they are used with imbalanced data. Hence, alternatives to the AUC have been proposed, such as the partial AUC and the area under the precision-recall curve. However, these alternatives cannot be as fully interpreted as the AUC, in part because they ignore some information about actual negatives.Methods: We derive and propose a new concordant partial AUC and a new partial c statistic for ROC data-as foundational measures and methods to help understand and explain parts of the ROC plot and AUC. Our partial measures are continuous and discrete versions of the same measure, are derived from the AUC and c statistic respectively, are validated as equal to each other, and validated as equal in summation to whole measures where expected. Our partial measures are tested for validity on a classic ROC example from Fawcett, a variation thereof, and two real-life benchmark data sets in breast cancer: the Wisconsin and Ljubljana data sets. Interpretation of an example is then provided.Results: Results show the expected equalities between our new partial measures and the existing whole measures. The example interpretation illustrates the need for our newly derived partial measures.Conclusions: The concordant partial area under the ROC curve was proposed and unlike previous partial measure alternatives, it maintains the characteristics of the AUC. The first partial c statistic for ROC plots was also proposed as an unbiased interpretation for part of an ROC curve. The expected equalities among and between our newly derived partial measures and their existing full measure counterparts are confirmed. These measures may be used with any data set but this paper focuses on imbalanced data with low prevalence.Future Work: Future work with our proposed measures may: demonstrate their value for imbalanced data with high prevalence, compare them to other measures not based on areas; and combine them with other ROC measures and techniques. [ABSTRACT FROM AUTHOR]

• A novel integrated framework for the diagnosis of breast cancer patients is proposed. • A scheme combining feature selection and hyperparameter optimization is proposed to improve the prediction accuracy of the model. • Compared with the existing models, the proposed model has higher performance and generalization ability. In this paper, a new integrated scheme is proposed to accurately predict breast cancer, help doctors make early diagnosis and treatment plans, and improve the prognosis of patients. We selects five mainstream machine learning models: support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost). The Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) are used as datasets to investigate the predictive performance of these single and ensemble models. Then, we use multiple linear regression method for feature selection (FS), the experimental results show that the change of feature subset will significantly affect the performance of the model. The recall and f1-score of the five models are improved by 1.19% and 0.84% on average. After that, we apply whale optimization algorithm (WOA) to optimize the hyperparameters of the model to improve their prediction performance. In the best-case scenario, the model demonstrated improvements of 1.02% in accuracy and 1.82% in precision. In addition, we ensemble these models by stacking, investigate the performance changes of the ensemble model when different models are used as meta learners. Finally, the FS-WOA-Stacking model achieves 99.56% accuracy on WBCD and 99.65% accuracy on WDBC. Compared with the existing breast cancer prediction models, the performance of the proposed model is at an excellent level. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for finding feature subsets that are effective for classification in an input dataset by using a multi-agent reinforcement learning method. In the field of machine learning, it is crucial to find features suitable for classification. A dataset may have numerous features; while some features may be effective for classification or prediction, others may have little or rather negative effects on results. In machine learning problems, feature selection for increasing classification or prediction accuracy is a critical problem. To solve this problem, we proposed a feature selection method based on reinforced learning. Each feature has one agent, which determines whether the feature is selected . After obtaining corresponding rewards for each feature that is selected, but not by the agents, the Q-value of each agent is updated by comparing the rewards. The reward comparison of the two subsets helps agents determine whether their actions were right. These processes are performed as many times as the number of episodes, and finally, features are selected. As a result of applying this method to the Wisconsin Breast Cancer, Spambase, Musk, and Colon Cancer datasets, accuracy improvements of 0.0385, 0.0904, 0.1252 and 0.2055 were shown, respectively, and finally, classification accuracies of 0.9789, 0.9311, 0.9691 and 0.9474 were achieved, respectively. It was proved that our proposed method could properly select features that were effective for classification and increase classification accuracy. [ABSTRACT FROM AUTHOR]

Previous research shows that feeding and activity behaviours in combination with machine learning algorithms has the potential to predict the onset of bovine respiratory disease (BRD). This study used 229 novel and previously researched feeding, movement, and social behavioural features with machine learning classification algorithms to predict BRD events in pre-weaned calves. Data for 172 group housed calves were collected using automatic milk feeding machines and ultrawideband location sensors. Health assessments were carried out twice weekly using a modified Wisconsin scoring system and calves were classified as sick if they had a Wisconsin score of five or above and/or a rectal temperature of 39.5 °C or higher. A gradient boosting machine classification algorithm produced moderate to high performance: accuracy (0.773), precision (0.776), sensitivity (0.625), specificity (0.872), and F1-score (0.689). The most important 30 features were 40% feeding, 50% movement, and 10% social behavioural features. Movement behaviours, specifically the distance walked per day, were most important for model prediction, whereas feeding and social features aided in the model's prediction minimally. These results highlighting the predictive potential in this area but the need for further improvement before behavioural changes can be used to reliably predict the onset of BRD in pre-weaned calves. [ABSTRACT FROM AUTHOR]

Introduction: The incidence of breast cancer has steadily risen over the years owing to changes in lifestyle and environment. Presently, breast cancer is one of the primary causes of cancer-related deaths among women, making it a crucial global public health concern. Thus, the creation of an automated diagnostic system for breast cancer bears great importance in the medical community. Objectives: This study analyses the Wisconsin breast cancer dataset and develops a machine learning algorithm for accurately classifying breast cancer as benign or malignant. Methods: Our research is a retrospective study, and the main purpose is to develop a high-precision classification algorithm for benign and malignant breast cancer. To achieve this, we first preprocessed the dataset using standard techniques such as feature scaling and handling missing values. We assessed the normality of the data distribution initially, after which we opted for Spearman correlation analysis to examine the relationship between the feature subset data and the labeled data, considering the normality test results. We subsequently employed the Wilcoxon rank sum test to investigate the dissimilarities in distribution among various breast cancer feature data. We constructed the feature subset based on statistical results and trained 7 machine learning algorithms, specifically the decision tree, stochastic gradient descent algorithm, random forest algorithm, support vector machine algorithm, logistics algorithm, and AdaBoost algorithm. Results: The results of the evaluation indicated that the AdaBoost-Logistic algorithm achieved an accuracy of 99.12%, outperforming the other 6 algorithms and previous techniques. Conclusion: The constructed AdaBoost-Logistic algorithm exhibits significant precision with the Wisconsin breast cancer dataset, achieving commendable classification performance for both benign and malignant breast cancer cases. [ABSTRACT FROM AUTHOR]

Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions. [ABSTRACT FROM AUTHOR]

Each year number of deaths is increasing extremely because of breast cancer. It is the most frequent type of all cancers and the major cause of death in women worldwide. Any development for prediction and diagnosis of cancer disease is capital important for a healthy life. Consequently, high accuracy in cancer prediction is important to update the treatment aspect and the survivability standard of patients. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. In this study, we applied five machine learning algorithms: Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbours (KNN) on the Breast Cancer Wisconsin Diagnostic dataset, after obtaining the results, a performance evaluation and comparison is carried out between these different classifiers. The main objective of this research paper is to predict and diagnosis breast cancer, using machine-learning algorithms, and find out the most effective whit respect to confusion matrix, accuracy and precision. It is observed that Support vector Machine outperformed all other classifiers and achieved the highest accuracy (97.2%).All the work is done in the Anaconda environment based on python programming language and Scikit-learn library. [ABSTRACT FROM AUTHOR]

Learning the customers' experience and behavior creates competitive advantages for any company over its rivals. The insurance industry is an essential sector in any developed economy and a better understanding of customers' risk profile is critical to decision making in all aspects of insurance operations. In this paper, we explore the idea of using copula-based dependence models to learn the hidden risk of policyholders in property insurance. Specifically, we build a novel copula model to accommodate the dependence over time and over space among spatially clustered property risks. To tackle the computational challenge caused by the discreteness feature of large-scale insurance data, we propose an efficient multilevel composite likelihood approach for parameter estimation. Provided that latent risk induces correlation, the proposed customer learning method offers improved predictive analytics by allowing insurers to borrow strength from related risks in predicting new risks and also helps reveal the relative importance of the multiple sources of unobserved heterogeneity in updating policyholders' risk profile. In the empirical study, we examine the loss cost of a portfolio of entities insured by a government property insurance program in Wisconsin. We find both significant temporal and spatial association among property risks. However, their effects on the predictive distribution of loss cost are different for the new and renewal policyholders. The two sources of dependence are complements for the former and substitutes for the latter. These findings are shown to have substantial managerial implications in key insurance operations such as experience rating, capital allocation, and reinsurance arrangement. [ABSTRACT FROM AUTHOR]

Cancer is typically a disease that is instigated when cells partition uncontrollably, thereby spread to surrounding tissues. Among different kinds of cancer, breast cancer, cervical and lung cancer have found to experience less screening rate. Hence, detecting such kinds of cancer has become crucial to save individual's life. With the ability of ML (Machine Learning) for solving complicated tasks in dynamic configurations have contributed to its application in prognosticating cancer with diverse meta-heuristic algorithms. Though conventional researches have attempted to perform this, feature selection process has been inefficient that negatively impacted the accuracy rate. To resolve such consequences, the study considers Wisconsin breast cancer dataset, lung cancer dataset and cervical cancer dataset from UCI ML repository and proposes Modified QPSO-LDA (Quantum Particle Swarm Optimization-Linear Discriminant Analysis) to select suitable and relevant features. Following this, WGB (Weighted Gradient Boosting) is proposed for classification that works based on weighted column subsampling. To validate the performance of this system in prognosticating breast cancer, cervical cancer and lung cancer, analysis is undertaken that confirm its effectiveness with 0.9912 as accuracy for breast cancer dataset, 0.99 as accuracy for cervical cancer dataset and 0.97 as accuracy for lung cancer dataset. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the largest cause of death in women. Accurate classification of breast cancer data is important in cancer diagnosis and classification of Malignant and Benign tumors can prevent patients to take unnecessary tests. Breast cancer classification can also be used to determine suitable treatment. Classification of Benign and Malignant patient groups is widely recognized research in the medical field. Due to the advantage of detecting critical features from a medical data set, machine learning is widely used in Breast cancer Prediction. Recently there has been greater attention to the use of machine learning methods in medical diagnosis. These decision support systems are effective and helpful for medical experts in the healthcare domain. The objective of this work is to address the problem of the classification of breast cancer data using ensemble learning. Ensemble learning techniques are used to improve the performance of a classifier. This paper deals with building a decision support system using the ensemble model built with Bayesian network and Radial Basis Function. In this work, extensive experiments were carried out on the much-studied open access data set "Wisconsin Breast Cancer Data set (WBCD)". The data set is partitioned into training and testing. Various metrics like accuracy, sensitivity, specificity, positive predictive value, negative predicted value, Error rate, false-positive rate, Mathew's correlation coefficient were used to measure the performance of the model. Experimental results show that the proposed method records a remarkable accuracy of 97% to classify breast cancer data and outperformed the existing approaches. The proposed ensemble learning would be viable in helping cancer specialists in recognizing cancer tumors accurately and help the patients in taking the correct treatment. [ABSTRACT FROM AUTHOR]

• A new ensemble-based classification model for breast cancer detection is proposed. • Benchmark clinical data set is utilized to check the effectiveness of the model. • Most important risk factors of breast cancer were extracted using SVM. • Quite interpretable and accurate clinical decision support is yielded by the model. This paper presents a new data mining technique for an accurate prediction of breast cancer (BC), which is one of the major mortality causes among women around the globe. The main objective of our study is to expand an automatic expert system (ES) to provide an accurate diagnosis of BC. Both, Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) were applied to analyze BC data. The well-known Wisconsin Breast Cancer Dataset (WBCD), available in the UCI repository, was examined in our study. We first tested the SVM algorithm using various values of the C , ɛ and γ parameters. As a result of the first experiment, we were able to observe that the adjustment of these regularization parameters can greatly improve the performance of the traditional SVM algorithm applied for BC detection. The highest obtained accuracy at the first step was 99.71%. Then, we performed a new BC detection approach based on two ensemble learning techniques: the confidence-weighted voting method and the boosting ensemble technique. Our model, called CWV-BANNSVM, combines boosting ANNs (BANN) and two SVMs, using optimal parameters selected during the first experiment. The performance of the applied methods was evaluated using several popular metrics, such as specificity, sensitivity, precision, FPR, FNR, F 1 score, AUC, Gini and accuracy. The proposed CWV-BANNSVM model was able to improve the performance of the traditional machine learning algorithms applied to BC detection, reaching the accuracy of 100%. To overcome the overfitting issue, we determined and used some appropriate parameter values of polynomial SVM. Our comparison with the existing studies dedicated to BC prediction suggests that the proposed CWV-BANN-SVM model provides one of the best prediction performances overall. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer-related death in women, accounting for 16% of all cancer-related fatalities globally. Breast cancer is fatal in just half of all cases. Radiologists may misread worrisome lesions due to imaging quality concerns and diverse breast densities, which raises the false-(positive and negative) ratio, as the primary explanation for the problem. Early intervention is critical in building a current prognosis process that can successfully limit disease consequences and increase recovery. Patients are being referred back for biopsies to dispel suspicions when the inconsistent feature-extraction approach is used for manual screening of breast abnormalities in traditional schemes. With the Multi-kernel Support Vector Machine (MKSVM), we develop a new modality for the prediction of breast cancer and train its properties using the supervised machine learning approaches. Furthermore, the IDA-MKSVM technique achieved maximum average accuracy is 95.75%, average sensitivity is 94.29%, average specificity is 95.16% and average F-score is 95.39% for different training datasets. For selecting ideal features, the Improved Dragonfly Algorithm (IDA) is also used. A 10-fold cross validation procedure is used in the system under consideration to ensure accuracy. The UCI machine learning repository holds the breast cancer diagnosis data referred to as the Wisconsin breast cancer diagnosis data set. [ABSTRACT FROM AUTHOR]

Effective disease diagnosis is a significant unmet need on a global scale. The development of a model for early diagnosis and efficient treatment faces enormous hurdles due to the complexity of the many disease mechanisms and underlying symptoms of the patient population. In recent times, various ensemble-based ML models are assisting doctors with early diagnosis. But one of the biggest challenges for these models is the learning selection of diversity-based classifiers to enhance the model's performance. Many researchers are attempted to improve classification accuracy with ensemble learning approaches but they failed. To improve the disease diagnosis performance in this study, a novel diversity-based evolutionary ensemble framework with a genetic algorithm is proposed. To improve the predictive performance we have used four diversity-based classifiers such as K-nearest neighbour, support vector machine, logistic regression, and decision tree using the bootstrapped approach to generate 20 diverse base learners with five bootstrapped bags using fivefold cross-validation. And also to improve the predictive performance, we have proposed a novel fitness function. To test the robustness, the model was run 20 times, and the average performance and average ensemble complexity of the proposed model were computed. Finally, our proposed model tested with four bench-mark disease datasets such as Pima Indian Diabetes (PID), Chronic Kidney Disease (CKD), Statlog Heart Data (SHD), and Wisconsin Breast Cancer (WBC). These results are compared with state-of-the-art ensemble models and non-ensemble models. Results demonstrated that in comparison with ensemble models, the proposed model performance is superior in terms of accuracy, AUC, and specificity on PID, accuracy, sensitivity, and specificity on CKD, accuracy, AUC, specificity on SHD, and accuracy, AUC, and specificity on WBC. In comparison with non-ensemble models, the proposed model performance is best in terms of accuracy, precision on PID, accuracy, AUC, precision and F1-score on CKD, accuracy, AUC, sensitivity, precision, and F1-score on SHD and accuracy, AUC on WBC. [ABSTRACT FROM AUTHOR]

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, Fmeasure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector. [ABSTRACT FROM AUTHOR]

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, Fmeasure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector. [ABSTRACT FROM AUTHOR]

In data mining, deep learning and machine learning models face class imbalance problems, which result in a lower detection rate for minority class samples. An improved Synthetic Minority Over-sampling Technique (SMOTE) is introduced for effective imbalanced data classification. After collecting the raw data from PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases, the pre-processing is performed using min-max normalization, cleaning, integration, and data transformation techniques to achieve data with better uniqueness, consistency, completeness and validity. An improved SMOTE algorithm is applied to the pre-processed data for proper data distribution, and then the properly distributed data is fed to the machine learning classifiers: Support Vector Machine (SVM), Random Forest, and Decision Tree for data classification. Experimental examination confirmed that the improved SMOTE algorithm with random forest attained significant classification results with Area under Curve (AUC) of 94.30%, 91%, 96.40%, and 99.40% on the PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases. [ABSTRACT FROM AUTHOR]

A machine learning methodology for processing and visualizing high‐resolution LiDAR digital data is used to map drumlins and mega‐scale glacial lineations (MSGLs) on the bed of the Late Wisconsin Green Bay Lobe in Wisconsin, USA, which exhibited surge‐like behaviour during deglaciation. Previous work has shown that streamlined bedforms are the product of erosional streamlining of pre‐existing sediment. Analysis of bedform height and elongation ratio using curvature‐based relief separation (CBRS) and K‐means clustering of 32,003 bedforms reveals a continuum of six morphotypes ranging from drumlins, through "channeled" more elongated multi‐crested drumlins, to MSGLs. Further statistical analysis shows that morphotypes cluster into six types of streamlined surfaces (S1–S6) recording progressive elimination of an antecedent overridden topography to produce a smoother bed. Initial, relatively high‐relief drumlinized surfaces (S1, S2) occur around the slower‐flowing lateral flanks of the lobe, where a pre‐existing hummocky morainal topography was only partially modified by subglacial erosion. More streamlined surfaces (S3, S4) dominated by multi‐crested more elongate drumlins of reduced relief amplitude are transitional to flow sets of MSGL‐dominated surfaces (S5, S6) indicative of much faster‐flowing ice streaming along the lobe's axis. Estimates of basal drag based on roughness calculations for each surface type identify a 61% reduction in frictional retardation from poorly streamlined surfaces S1 and S2 to MSGL‐dominated surfaces S5 and S6, with a step‐like reduction between drumlins and channeled drumlins (S3, S4) possibly recording the rapid onset of fast flow. Subglacial streamlining is argued to be accomplished by a thin (<1 m) "third layer" of deforming subglacial debris between ice and its bed which functioned as an erodent layer. A thin (<3 m) till cap, formed by aggradation of deforming debris, rests unconformably on heterogeneous core sediments. Streamlined subglacial surfaces are comparable to the "functional" surfaces resulting from erosion by a "third layer" of wear debris in engineering tribological systems, and also by gouge on faults. Pleistocene ice sheets expanded over pre‐existing landsystems, pointing to the broader relevance of the methodology and findings reported here. [ABSTRACT FROM AUTHOR]

This work aims to identify cancerous (malignant) and non‐malignant (non‐cancerous) cells in a breast cancer database. Wisconsin breast cancer data (WBC) was utilized and obtained from the University of California, Irvine's machine learning repository. The proposed approach involves the Naive bayes algorithm with Gaussian distribution of the function in combination with Chi‐squared‐based attributes selection approach. This experimentation has been done after reducing the dimensional space of the used data with extended Kernel Principal Component Analysis (K‐PCA). Five different kernels in K‐PCA have been tested after the implementation of necessary pre‐processing techniques. The performance assessment of the proposed system has been evaluated based on confusion matrix‐based accuracy, precision, sensitivity, and specificity. Our proposed methodology with six selected feature and sigmoid K‐PCA attained the best accuracy of 99.28%. This result outer performs many state‐of‐the‐art studies recently published on the identical dataset. [ABSTRACT FROM AUTHOR]

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Concrete is the most widely used construction material throughout the world. Extensive experiments are conducted every year to study the physical, mechanical, and chemical properties of concrete involving a hefty amount of money and time. This work focuses on the utilization of Machine Learning (ML) algorithms to predict various concrete properties for avoiding unnecessary experimentation. In this work, six mechanical properties of concrete namely modulus of rupture, compressive strength, modulus of elasticity, Poisson's ratio, splitting tensile strength, and coefficient of thermal expansion are estimated by applying five different ML algorithms viz. Linear Regression, Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosting models on the Wisconsin concrete mixes database. Further, these ML models were evaluated to identify the most suitable model that can reliably predict the mechanical properties of concrete. The approach followed in this research was verified using the 10-fold Cross-Validation technique to eliminate training and testing split bias. The Grid Search Cross Validation method was used to find the best hyperparameters for each algorithm. Root mean squared error (RMSE) and coefficient of determination (R2) results showed that the Support Vector Machine outperformed the other models applied on the datasets. Support Vector Machine predicted the modulus of rupture of concrete after a curing time of 28 days with an R2 score of 0.43, which is better than the R2 scores of Random Forest and Gradient Boosting advanced algorithms by 34% and 26%, respectively. [ABSTRACT FROM AUTHOR]

Although most cases are identified at a late stage, breast cancer is the most public malignancy amongst women globally. However, mammography for the analysis of breast cancer is not routinely available at all general hospitals. Prolonging the period between detection and treatment for breast cancer may raise the likelihood of proliferating the disease. To speed up the process of diagnosing breast cancer and lower the mortality rate, a computerized method based on machine learning was created. The purpose of this investigation was to enhance the investigative accuracy of machine-learning algorithms for breast cancer diagnosis. The use of machine-learning methods will allow for the classification and prediction of cancer as either benign or malignant. This investigation applies the machine learning algorithms of random forest (RF) and the support vector machine (SVM) with the feature extraction method of linear discriminant analysis (LDA) to the Wisconsin Breast Cancer Dataset. The SVM with LDA and RF with LDA yielded accuracy results of 96.4% and 95.6% respectively. This research has useful applications in the medical field, while it enhances the efficiency and precision of a diagnostic system. Evidence from this study shows that better prediction is crucial and can benefit from machine learning methods. The results of this study have validated the use of feature extraction for breast cancer prediction when compared to the existing literature. [ABSTRACT FROM AUTHOR]

One of the most prevalent and leading causes of cancer in women is breast cancer. It has now become a frequent health problem, and its prevalence has recently increased. The easiest approach to dealing with breast cancer findings is to recognize them early on. Early detection of breast cancer is facilitated by computer-aided detection and diagnosis (CAD) technologies, which can help people live longer lives. The major goal of this work is to take advantage of recent developments in CAD systems and related methodologies. In 2011, the United States reported that one out of every eight women was diagnosed with cancer. Breast cancer originates as a result of aberrant cell division in the breast, which leads to either benign or malignant cancer formation. As a result, early detection of breast cancer is critical, and with effective treatment, many lives can be saved. This research covers the findings and analyses of multiple machine learning models for identifying breast cancer. The Wisconsin Breast Cancer Diagnostic (WBCD) dataset was used to develop the method. Despite its small size, the dataset provides some interesting data. The information was analyzed and put to use in a number of machine learning models. For prediction, random forest, logistic regression, decision tree, and K-nearest neighbor were utilized. When the results are compared, the logistic regression model is found to offer the best results. Logistic regression achieves 98% accuracy, which is better than the previous method reported. [ABSTRACT FROM AUTHOR]

The diagnosis of breast cancer (BC) with a machine-learning model is a classification problem where the model involves training a model to identify the class of a given observation. However, real-world Wisconsin's BC diagnostic dataset, which is widely employed to implement a model for BC detection, consists imbalanced class. The benign class outnumbers the malignant class. The implementation of a model for BC detection with an imbalanced dataset leads to biased classification towards the majority class leading to lower accuracy and precision of malignant class. Thus, this research proposes a cost-sensitive logistic regression model for BC detection. During the training phase, benign and malignant class is weighted to influence the classification bias toward begin class. The study compared the model with standard logistic regression. The experimental result appears to prove that the proposed model outperforms as compared to standard logistic regression. The model has receiver characteristic curve area value AUC = 99.99. [ABSTRACT FROM AUTHOR]

Today, machine learning algorithms are an important research area capable of analyzing and modeling data in any field. Information obtained through machine learning methods helps researchers and planners to understand and review systematic problems of their current strategies. Thus, it is very important to work fully in every field that facilitates human life, such as early and correct diagnosis, correct choice, fully functioning autonomous systems. In this paper, a novel machine learning algorithm for multiclass classification is presented. The proposed method is designed based on the Minimum Distance Classifier (MDC) algorithm. The MDC is variance-insensitive because it classifies input vectors by calculating their distances/similarities with respect to class-centroids (average value of input vectors of a class). As it is known, real-world data contains certain proportions of noise. This situation negatively affects the performance of the MDC. To overcome this problem, we developed a variance-sensitive model, which we call Standardized Variable Distances (SVD), considering the standard deviation and z-score (standardized variable) factors. To ensure the accuracy of the SVD, we used Wisconsin Breast Cancer Original (WBCO) and LED Display Domain (led7digit) datasets, which we obtained from UCI machine learning repository, with 5-fold cross validation. It was compared and analyzed classification performance of the SVD with Decision Tree (DT), Random Forest (RF), k-Nearest Neighbor (k-NN), Multinomial Logistic Regression (MLR), Naïve Bayes (NB), Support Vector Machine (SVM), and the Minimum Distance Classifier (MDC), which are well-known in the literature. It has also been compared thirteen different studies using the same datasets over the past five years. Our results in the experimental studies have shown that the SVD can classify better than traditional and state-of-the-art methods, compared in this study. The proposed method reached over 97% classification accuracy (CACC), F-measure (FM) and area under the curve (AUC) on the WBCO dataset. On the led7digit dataset, approximately 74% CACC, 75.1% FM and 82.2% AUC scores were obtained. It has been observed that the classification scores obtained with the SVD are higher than other ML algorithms used in the experimental studies. • The Standardized Variable Distances (SVD) is a novel machine learning algorithm. • The SVD is an improved version of the Minimum Distance Classifier (MDC) algorithm. • The SVD, which is a distance-based method, is capable of multiclass classification. • The SVD can be coupled with any distance measure method. [ABSTRACT FROM AUTHOR]

The primary motivation of this paper is twofold: first, to employ a heuristic optimization algorithm to optimize the dendritic neuron model (DNM) and second, to design a tidy visual classifier for computer-aided diagnosis that can be easily implemented on a hardware system. Considering that the backpropagation (BP) algorithm is sensitive to the initial conditions and can easily fall into local minima, we propose an evolutionary dendritic neuron model (EDNM), which is optimized by the gbest-guided artificial bee colony (GABC) algorithm. The experiments are performed on the Liver Disorders Data Set, the Wisconsin Breast Cancer Data Set, the Haberman's Survival Data Set, the Diabetic Retinopathy Debrecen Data Set and Hepatitis Data Set, and the effectiveness of our model was rigorously validated in terms of the classification accuracy, the sensitivity, the specificity, the F_measure, Cohen's Kappa, the area under the receiver operating characteristic curve (AUC), convergence speed and the statistical analysis of the Wilcoxon signed-rank test. Moreover, after training, the EDNM can simplify its neural structure by removing redundant synapses and superfluous dendrites by the neuronal pruning mechanism. Finally, the simplified structural morphology of the EDNM can be replaced by a logic circuit (LC) without sacrificing accuracy. It is worth emphasizing that once implemented by an LC, the model has a significant advantage over other classifiers in terms of speed when handling big data. Consequently, our proposed model can serve as an efficient medical classifier with excellent performance. [ABSTRACT FROM AUTHOR]