An analytical method for diseases prediction using machine learning techniques

Abstract

The use of medical datasets has attracted the attention of researchers worldwide. Data mining techniques have been widely used in developing decision support systems for diseases prediction through a set of medical datasets. In this paper, we propose a new knowledge-based system for diseases prediction using clustering, noise removal, and prediction techniques. We use Classification and Regression Trees (CART) to generate the fuzzy rules to be used in the knowledge-based system. We test our proposed method on several public medical datasets. Results on Pima Indian Diabetes, Mesothelioma, WDBC, StatLog, Cleveland and Parkinson's telemonitoring datasets show that proposed method remarkably improves the diseases prediction accuracy. The results showed that the combination of fuzzy rule-based, CART with noise removal and clustering techniques can be effective in diseases prediction from real-world medical datasets. The knowledge-based system can assist medical practitioners in the healthcare practice as a clinical analytical method.