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- W2794626629 abstract "Feature selection is popular problem in the classification of diseases in clinical medicine. Here, we developing a hybrid methodology to classify diseases, based on three medical datasets, Arrhythmia, Breast cancer, and Hepatitis datasets. This methodology called k-means ANOVA Support Vector Machine (K-ANOVA-SVM) uses K-means cluster with ANOVA statistical to preprocessing data and selection the significant features, and Support Vector Machines in the classification process. To compare and evaluate the performance, we choice three classification algorithms, decision tree Naive Bayes, Support Vector Machines and applied the medical datasets direct to these algorithms. Our methodology was a much better classification accuracy is given of 98% in Arrhythmia datasets, 92% in Breast cancer datasets and 88% in Hepatitis datasets, Compare to use the medical data directly with decision tree Naive Bayes, and Support Vector Machines. Also, the ROC curve and precision with (K-ANOVA-SVM) Achieved best results than other algorithms" @default.
- W2794626629 created "2018-04-06" @default.
- W2794626629 creator A5051521369 @default.
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- W2794626629 date "2018-03-01" @default.
- W2794626629 modified "2023-09-24" @default.
- W2794626629 title "A hybrid approach to select features and classify diseases based on medical data" @default.
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- W2794626629 doi "https://doi.org/10.1088/1757-899x/322/6/062002" @default.
- W2794626629 hasPublicationYear "2018" @default.
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