Applications of dynamic feature selection and clustering methods to medical diagnosis

Machine learning methods are commonly used for disease and cancer diagnosis. The model perfor-mance can be improved via feature selection, feature reduction, and clustering methods. Although these supplementary techniques have certain advantages, they cannot necessarily guarantee better performance. The objective of this study is to improve the performance of classification methods used for medical diagnosis of various diseases. We propose a dynamic feature selection method based on the merits of both principal component analysis and Wrapper feature selection methods. It is a novel multi-objective feature selection method based on a customized genetic algorithm that is guided by eigenvalues of the features and feedbacks of various classifiers' output. To reinforce classification learning and further enhancement of their performance, we utilize a dynamic selection of three clustering methods including K-means, fuzzy c-means, and particle swarm optimization. We also investigated the performance of two deep learning classifiers on the proposed methods. To show the impacts of combination of the proposed methods, we analyze the results of applying 12 machine learning and two deep learning classifiers to 30 imbalanced medical datasets. According to our extensive computational experiments and the statistical tests, the proposed dynamic feature selection and clustering methods perform significantly better than existing methods. The proposed methods not only improve the average of performance measures by 5% but also are more accurate than best performing classification methods in the literature used for the same datasets. (c) 2022 Elsevier B.V. All rights reserved.