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Early stage of prediction and diagnosis is the only way to solve the challenges due to medical data. Machine learning tools and techniques help in prediction and diagnosis of different types of medical data. In this paper, Simplex Method based Social Spider Optimization (SMSSO) method is used which modifies the Social Spider Optimization (SSO) method. Different types of datasets were used to validate the SMSSO-NN technique. SMSSO-NN shows 99.36%, 94%, 95.78%, and 98% accuracy in Wisconsin Breast Cancer (WBC), Lung cancer, Diabetic, and Cervical cancer datasets, respectively that is better than other methods. The accuracy of the SMSSO-NN algorithm is associated with different classification techniques like Structured Singular Value (SSV), Local Linear Wavelet Neural Network-Firefly Algorithm (LLWNN-FA), and Rough Set Theory. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first part, the three classifiers are trained simultaneously on the learning dataset. The output of the first part classifier together with the learning dataset is placed in a new dataset. This dataset uses a hybrid classifier method to model the mapping between the outputs of each ordinary classifier of the first part with real output labels. The proposed algorithm is implemented with three different variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP and gradient descent with momentum for fine tuning of the weight of MLP neural network and their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLP-RP produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the methods titled GP-DLNN, GAANN and CAFS and other works found in the literature. [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]

The hybrid of artificial neural network (ANN) and fuzzy logic system (FLS) can expend itself dynamically in a strong discovery of explicit knowledge to solve classification and regression problems with new input patterns. In this paper, a hybrid of Generalized Adaptive Resonance Theory (GART) and interval type-2 fuzzy logic system (IT2FLS) algorithm is proposed, and named as Generalized Adaptive Resonance Theory and interval type-2 fuzzy logic system (GART-IT2FLS). The GART is a combination of adaptive resonance theory network (ART) and Generalized Regression Neural Network (GRNN). GART is capable to deal with classification task effectively. However, type-2 fuzzy sets (T2 FS) is used to represent and model the uncertainties on inputs. The performance evaluation of GART-IT2FLS algorithm in three medical datasets has proven that GART-IT2FLS is capable to learn incrementally without plasticity-stability dilemma, and model uncertainties in medical datasets. The inferences of GAR-IT2FLS in these applications are discussed. The performance results show that GART-IT2FLS has obtained a comparable number of rules. The Wisconsin Breast Cancer and Heart Disease experiments demonstrated GART-IT2FLS has improved the testing accuracies. [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 has become one of the leading causes of death in female population due to its high morbidity and mortality. However, the treatment options for benign or malignant tumors are different, which makes the diagnosis of breast cancer important. In this paper, the subset method is proposed to improve the single-output Chebyshev-polynomial neural network (SOCPNN), and the modified SOCPNN has a 100.00% testing accuracy in pattern classification on the Wisconsin breast cancer dataset. Specifically, the subset method generates the optimal number of cross-validation folds and constructs the initial structure of the modified SOCPNN automatically and rapidly, with a group of basis functions based on Chebyshev-polynomials exploited to activate the neural network. In addition, the weights of the hidden-layer neurons are directly determined by the weights-direct-determination (WDD) method. Besides, the optimal structure of the neural network is determined by a growing method. Moreover, to improve the generalization performance, the multi-fold cross-validation algorithm is exploited in the modified SOCPNN. Finally, comparative experiments on the Wisconsin breast cancer dataset are conducted and the results show that the modified SOCPNN has higher accuracy in classification of breast cancer on this dataset compared to the traditional machine learning algorithms. [ABSTRACT FROM AUTHOR]