JUCS - Journal of Universal Computer Science 24(6): 775-796, doi: 10.3217/jucs-024-06-0775
Medical Diagnosis of Chronic Diseases Based on a Novel Computational Intelligence Algorithm
expand article infoYenny Villuendas-Rey, Mariana-D. Alanis-Tamez§, Carmen-F. Rey Benguría|, Cornelio Yáñez-Márquez§, Oscar Camacho-Nieto
‡ CIDETEC-IPN, Ciudad de Mexico, Mexico§ CIC-IPN, Ciudad de Mexico, Mexico| Universidad de Ciego de Ávila, Ciego de Ávila, Cuba
Open Access
Abstract
Computational Intelligence techniques in medicine have become an increasing area of research worldwide. Among them, the application and development of new models and algorithms for disease diagnosis and prediction have been an active research topic. The research contribution of the current paper is the proposal of a novel classification model, and its application to the diagnosis of chronic diseases. One of the main characteristics of the new model is that it is designed to deal with imbalanced data. With the purpose of making experimental comparisons to demonstrate the benefits of the proposed model, 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 in order to identify significant differences in performance by mean of a non-parametric statistical test (the Friedman test), and a post-hoc test (the Holm test). The hypothesis testing analysis of the experimental results indicates that the proposed model outperforms other supervised classifiers for medical diagnosis.
Keywords
medical informatics, disease prediction and diagnosis, computational intelligence