Computational Intelligence Algorithms Applied to the Pre-diagnosis of Chronic Diseases

Abstract

Classification models applied to medicine have become an increasing area of research worldwide. Such as, the application and development of known models and algorithms for disease diagnosis and prediction have been an active research topic. The present article is a study of the classification algorithms most used in the literature, and its application to the diagnosis of chronic diseases. More specifically, we tested five classification models, over medical data. The application of the supervised classification algorithms is done over the Knowledge Extraction based on Evolutionary Learning (KEEL) environment, using a Distributed optimally balanced stratified 5-fold cross validation scheme. In addition, the experimental results obtained were validated to identify significant differences in performance by mean of a non-parametric statistical test (the Friedman test). The hypothesis testing analysis of the experimental results indicates which supervised classification model outperforms others for medical diagnosis.