Predicting Chronic Kidney Disease Using Filter and Wrapper Feature Selection Methods with Machine Learning Techniques
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Abstract
Chronic kidney disease (CKD) is a condition characterized by the gradual loss of kidney function over months or years. Predicting this disease is a crucial issue in the medical field. Therefore, an automated tool utilizing Machine Learning (ML) techniques to assess a patient's kidney condition would be beneficial for doctors in predicting CKD and improving treatment. In the ML process, the preprocessing stage is a vital step that enhances data quality. Feature selection, a key preprocessing method, removes irrelevant or redundant features, thereby simplifying the model and reducing the number of features. This research explores the potential of various feature selection methods. The feature selection methods are categorized into filter methods (f_classif, chi2) and wrapper methods (Recursive Feature Elimination with Cross-Validation RFECV) using Random Forest classifier and Support Vector Machine. The accuracy of classifiers was used to evaluate the performance of the full dataset compared to subsets created using feature selection (FS). The results show that the RFECV+SVM feature selection method outperforms others, yielding the best performance by improving accuracy in 5 out of 6 classifiers.
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References
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