Search

Osteoporosis, also known as low bone mineral density (BMD), is a serious health concern, especially for women who have gone through menopause in community settings. This condition weakens the bones and increases the risk of fractures. Women who have gone through menopause are more susceptible to osteoporosis due to hormonal changes. Therefore, it is crucial to identify the condition early to start preventive treatments and reduce the risk of fractures. To address the challenges of diagnosing low BMD in postmenopausal women in community settings, this study proposes a method that combines machine learning with the AdaBoostM1 algorithm, which has shown promising results. Data acquisition, data preprocessing, data training, model testing, and model prediction and evaluation are integral phases of the operational dynamics of our model in osteoporosis diagnosis. This approach recommends increasing screening initiatives and educating patients as strategies to improve early detection and management of the disease. The analysis method used achieved an impressive accuracy rate of approximately 88.8% on the dataset it was applied to. The area under the curve was 0.87, the true positive rate was 88%, and the F1 measure was 0.88. By using accurate diagnostic techniques and providing proactive community care, the incidence of osteoporotic fractures can be significantly reduced, thus improving the quality of life for this vulnerable population. [ABSTRACT FROM AUTHOR]

Skin cancer is the irregular growth of skin cells, which is most often termed as cancer, developed by exposure of ultraviolet rays from sun. In this research paper, deep learning enabled hybrid optimization is followed for skin cancer detection and lesion segmentation. Two optimization algorithms are followed for skin lesion segmentation and cancer detection. Here, pre-processing is done by anisotropic diffusion followed by skin lesion segmentation. Here, Multi-Scale Residual Fusion Network (MSRFNet) is utilized for skin lesion segmentation, which is trained by proposed Average Subtraction Student Psychology Based Optimization (ASSPBO). After skin lesion segmentation, necessary features are extracted, followed by skin cancer detection. Skin cancer is detected by Deep Residual Network (DRN) trained by proposed Fractional ASSPBO (FrASSPBO). Moreover, performance of proposed FrASSPBO-DRN is analysed by three performance metrics like testing accuracy, True Positive Rate (TPR), and False Positive Rate (FPR) with values of 93.4%, 94%, and 8.2%. [ABSTRACT FROM AUTHOR]

Hydraulic systems are advanced in function and level as they are used in various industrial fields. Furthermore, condition monitoring using internet of things (IoT) sensors is applied for system maintenance and management. In this study, meaningful features were identified through extraction and selection of various features, and classification evaluation metrics were presented through machine learning and deep learning to expand the diagnosis of abnormalities and defects in each component of the hydraulic system. Data collected from IoT sensor data in the time domain were divided into clusters in predefined sections. The shape and density characteristics were extracted by cluster. Among 2335 newly extracted features, related features were selected using correlation coefficients and the Boruta algorithm for each hydraulic component and used for model learning. Linear discriminant analysis (LDA), logistic regression, support vector classifier (SVC), decision tree, random forest, XGBoost, LightGBM, and multi-layer perceptron were used to calculate the true positive rate (TPR) and true negative rate (TNR) for each hydraulic component to detect normal and abnormal conditions. Valve condition, internal pump leakage, and hydraulic accumulator data showed TPR performance of 0.94 or more and a TNR performance of 0.84 or more. This study's findings can help to determine the stable and unstable states of each component of the hydraulic system and form the basis for engineers' judgment. [ABSTRACT FROM AUTHOR]

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that ϕ (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method. [ABSTRACT FROM AUTHOR]

A Bloom filter is a special case of an artificial neural network with two layers. Traditionally, it is seen as a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, and therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called "autoscaling Bloom filters", which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. Thus, by relaxing the requirement on perfect true positive rate, the proposed autoscaling Bloom filter addresses the major difficulty of Bloom filters with respect to their scalability. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of its performance and provide a procedure for minimizing its false positive rate. [ABSTRACT FROM AUTHOR]

Over the years, researchers have continued to put forward solutions to lessen the threats faced by whales within their ecosystem. The correct detection of the different species of whale is important in the search for solutions that will lessen the threats. In order to accurately detect and classify whale species, a number of techniques have been proposed over the years, with varying degrees of success. This research seeks to improve the performance of the hidden Markov models (HMMs), which is one of the most consistent methods for the detection and classification of whale vocalisations. The performance of HMMs is affected by the quality of the feature vectors fed into them. Thus, this research proposes feature extraction (FE) techniques based on principal component analysis. The principal components (PC) computed from PCA and kernel PCA were uniquely transformed into feature vector structures suitable for the HMMs. The emerging models, PCA-HMMs and kPCA-HMMs, were experimented with on passive acoustic monitoring (PAM) datasets containing southern right whale and humpback whale vocalisations. The results from the experiments showed that the kPCA-HMMs outperformed PCA-HMMs. This is due to kPCA's ability to find non-linear subspaces that may exist in whale vocalisations. Furthermore, the results of the developed PC-HMMs were compared with other existing FE techniques used with HMMs in the literature for the detection of whale vocalisations. The proposed PC-HMMs did not only outperform the existing FE-HMMs but also offered lower computational complexity than the existing HMMs for the detection of whale vocalisations. • Developed models for the automatic detection of whale species. • Whale features are extracted using PCA and kernel PCA. • Features are used with HMM for the detection of humpback and southern right whale. • Methodology can be modified for automatic detection of other vocalising animals. • Models contribute to whale conservation efforts. [ABSTRACT FROM AUTHOR]

The introduction of dedicated phlebotomy teams certified for blood collection has been reported to be highly cost-effective by reducing contamination rates. However, data on their effects on blood volume and true positive rate are limited. Therefore, we investigated the effect of replacing interns with a phlebotomy team on blood culture results. We performed a 24-month retrospective, quasi-experimental study before and after the introduction of a phlebotomy team dedicated to collecting blood cultures in a 2700-bed tertiary-care hospital. The microbiology laboratory database was used to identify adult patients with positive blood culture results. During the study period, there were no changes in blood collection method, blood culture tubes, and the application of antiseptic measures. Blood volume was measured by the BACTEC™ FX system based on red blood cell metabolism. A total of 162,207 blood cultures from 23,563 patients were analyzed, comprising 78,673 blood cultures during the intern period and 83,534 during the phlebotomy team period. Blood volume increased from a mean of 2.1 ml in the intern period to a mean of 5.6 ml in the phlebotomy team period (p < 0.001). Introduction of the phlebotomy team also reduced contamination rate (0.27% vs. 0.45%, p < 0.001) and led to a higher true positive rate (5.87% vs. 5.01%, p < 0.05). The increased true positive rate associated with the phlebotomy team involved both gram-positive and gram-negative bacteria. The introduction of a dedicated phlebotomy team can increase blood volumes, reduce blood culture contamination rate, and increase true positive rate. [ABSTRACT FROM AUTHOR]

Simultaneous control on true positive rate and false positive rate is of significant importance in the performance evaluation of diagnostic tests. Most of the established literature utilizes partial area under receiver operating characteristic (ROC) curve with restrictions only on false positive rate (FPR), called FPR pAUC, as a performance measure. However, its indirect control on true positive rate (TPR) is conceptually and practically misleading. In this paper, a novel and intuitive performance measure, named as two-way pAUC, is proposed, which directly quantifies partial area under ROC curve with explicit restrictions on both TPR and FPR. To estimate two-way pAUC, we devise a nonparametric estimator. Based on the estimator, a bootstrap-assisted testing method for two-way pAUC comparison is established. Moreover, to evaluate possible covariate effects on two-way pAUC, a regression analysis framework is constructed. Asymptotic normalities of the methods are provided. Advantages of the proposed methods are illustrated by simulation and Wisconsin Breast Cancer Data. We encode the methods as a publicly available R package tpAUC. [ABSTRACT FROM AUTHOR]

Prevalent sampling is frequently a convenient and economical sampling technique for the collection of time-to-event data and thus is commonly used in studies of the natural history of a disease. However, it is biased by design because it tends to recruit individuals with longer survival times. This paper considers estimation of time-dependent receiver operating characteristic curves when data are collected under prevalent sampling. To correct the sampling bias, we develop both nonparametric and semiparametric estimators using extended risk sets and the inverse probability weighting techniques. The proposed estimators are consistent and converge to Gaussian processes, while substantial bias may arise if standard estimators for right-censored data are used. To illustrate our method, we analyze data from an ovarian cancer study and estimate receiver operating characteristic curves that assess the accuracy of the composite markers in distinguishing subjects who died within 3-5 years from subjects who remained alive. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The food industry is one of the industries that uses machine vision for a nondestructive quality evaluation of the produce. These quality measuring systems and softwares are precalculated on the basis of various image-processing algorithms which generally use a particular type of classifier. These classifiers play a vital role in making the algorithms so intelligent that it can contribute its best while performing the said quality evaluations by translating the human perception into machine vision and hence machine learning. The crop of interest is rice, and the color of this crop indicates the health status of the plant. An enormous number of classifiers are available to solve the purpose of color prediction, but choosing the best among them is the focus of this paper. Performance of a total of 60 classifiers has been analyzed from the application point of view, and the results have been discussed. The motivation comes from the idea of providing a set of classifiers with excellent performance and implementing them on a single algorithm for the improvement of machine vision learning and, hence, associated applications. [ABSTRACT FROM AUTHOR]

The paper challenges the widespread use of Altman’s bankruptcy formula known as the “Z-score model” in Slovak corporate practice and comes with the goal to verify its usability in the Slovak economic environment. To this end, a definition of financial distress is adopted that summarizes weaknesses of Slovak enterprises stemming particularly from liquidity drain and operating losses. The verification juxtaposes three variants of the Z-score model and assesses their prediction ability using a data set of Slovak enterprises for the period from 2009 until 2013. Both the original 1968 Z-score model and the revised 1983 Z-score devised for the US economic environment are compared with the Z-score model re-estimated to the Slovak data copying the methodological procedure of Altman. The results indicate that Altman’s bankruptcy formula is portable into the Slovak economic conditions and useful for predicting financial difficulties in view of the adopted definition of financial distress. Altman’s original and (especially the) revised formulation of the Z-score model are preferable if overall classification accuracy is the main interest. Finally, it is advisable to re-estimate the coefficients of the Z-score model if financially distressed enterprises are the focus and the goal is to classify distressed enterprises as best as possible. [ABSTRACT FROM PUBLISHER]

Imbalanced data brings biased classification and causes the low accuracy of the classification of the minority class. In this article, we propose a methodology to select grouped variables using the area under the ROC with an adjustable prediction cut point. The proposed method enhance the accuracy of classification for the minority class by maximizing the true positive rate. Simulation results show that the proposed method is appropriate for both the categorical and continuous covariates. An illustrative example of the analysis of the SHS data in TCM is discussed to show the reasonable application of the proposed method. [ABSTRACT FROM AUTHOR]

It is well known that statistical classifiers trained from imbalanced data lead to low true positive rates and select inconsistent significant variables. In this article, an improved method is proposed to enhance the classification accuracy for the minority class by differentiating misclassification cost for each group. The overall error rate is replaced by an alternative composite criterion. Furthermore, we propose an approach to estimate the tuning parameter, the composite criterion, and the cut-point simultaneously. Simulations show that the proposed method achieves a high true positive rate on prediction and a good performance on variable selection for both continuous and categorical predictors, even with highly imbalanced data. An illustrative example of the analysis of the suboptimal health state data in traditional Chinese medicine is discussed to show the reasonable application of the proposed method. [ABSTRACT FROM AUTHOR]

High dimensionality of text data hinders the performance of classifiers making it necessary to apply feature selection for dimensionality reduction. Most of the feature ranking metrics for text classification are based on document frequencies ( df ) of a term in positive and negative classes. Considering only document frequencies to rank features favors terms frequently occurring in larger classes in unbalanced datasets. In this paper we introduce a new feature ranking metric termed as relative discrimination criterion (RDC), which takes document frequencies for each term count of a term into account while estimating the usefulness of a term. The performance of RDC is compared with four well known feature ranking metrics, information gain (IG), CHI squared (CHI), odds ratio (OR) and distinguishing feature selector (DFS) using support vector machines (SVM) and multinomial naive Bayes (MNB) classifiers on four benchmark datasets, namely Reuters, 20 Newsgroups and two subsets of Ohsumed dataset. Our results based on macro and micro F1 measures show that the performance of RDC is superior than the other four metrics in 65% of our experimental trials. Also, RDC attains highest macro and micro F1 values in 69% of the cases. [ABSTRACT FROM AUTHOR]

Background Voxel-based analyses are pervasive across the range of neuroimaging techniques. In the case of perfusion imaging using arterial spin labelling (ASL), a low signal-to-noise technique, there is a tradeoff between the contrast-to-noise required to detect a perfusion abnormality and its spatial localisation. In exploratory studies, the use of an a priori region of interest (ROI), which has the benefit of averaging multiple voxels, may not be justified. Thus the question considered in this study pertains to the sample size that is required to detect a voxel-level perfusion difference between groups and two algorithms are considered. New method Empirical 3T ASL data were acquired from 25 older adults and simulations were performed based on the group template cerebral blood flow (CBF) images. General linear model (GLM) and permutation-based algorithms were tested for their ability to detect a predefined hypoperfused ROI. Simulation parameters included: inter and intra-subject variability, degree of hypoperfusion and sample size. The true positive rate was used as a measure of sensitivity. Results For a modest group perfusion difference, i.e., 10%, 37 participants per group were required when using the permutation-based algorithm, whereas 20 participants were required for the GLM-based algorithm. Comparison with existing methods This study advances the perfusion power calculation literature by considering a voxel-wise analysis with correction for multiple comparison. Conclusions The sample size requirement to detect group differences decreased exponentially in proportion to increased degree of hypoperfusion. In addition, sensitivity to detect a perfusion abnormality was influenced by the choice of algorithm. [ABSTRACT FROM AUTHOR]

Compound identification is a critical process in metabolomics. The widely used approach for compound identification in gas chromatography-mass spectrometry-based metabolomics is spectrum matching, in which the mass spectral similarity between an experimental mass spectrum and each mass spectrum in a reference library is calculated. While various similarity measures have been developed to improve the overall accuracy of compound identification, little attention has been paid to reducing the false discovery rate. We, therefore, develop an approach for controlling the false identification rate using the distribution of the difference between the first and second highest spectral similarity scores. We further propose a model-based approach to achieving a desired true positive rate. The developed method is applied to the National Institute of Standards and Technology mass spectral library, and its performance is compared with that of the conventional approach that uses only the maximum spectral similarity score. The results show that the developed method achieves a significantly higher F1 score and positive predictive value than did the conventional approach. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: Diagnostic reviews often include the sensitivity/specificity results of individual studies. A problem occurs when these data are pooled because the correlation between sensitivity and specificity is generally strongly negative, causing overestimation of the pooled results. The diagnostic odds ratio (DOR), defined as the odds of true positives vs. that of false positives, may avoid this problem. The aim of the study was to review the advantages and limitations of the DORs. Methods: A systematic review of 44 previously published diagnostic studies was used as an example. Results: DORs can be readily implemented in diagnostic research. Advantages include: (1) they adjust for the negative and curvilinear correlations between sensitivities and specificities, (2) they take account of the heterogeneity between studies with respect to the different thresholds chosen by the investigators in the original studies, and (3) it is easy to extend the model with covariates representing between-study differences in design. Limitations include: 1) the outcome parameter is a summary estimate of both sensitivity and specificity, and 2) the magnitude of the studies included is not taken into account. Conclusions: Reported sensitivities and specificities of different studies assessing similar diagnostic tests are not only negatively correlated, but also negatively correlated in a curvilinear manner. It is appropriate to take this negative curvilinear correlation into account in the data pooling of such meta-analyses. The DORs can be applied for that purpose. Clin Chem Lab Med 2009;47:1351–4. [ABSTRACT FROM AUTHOR]