A hybrid approach to medical decision support systems: combining feature selection, fuzzy weighted pre-processing and AIRS

This paper presents a hybrid approach based on feature selection, fuzzy weighted pre-processing and artificial immune recognition system (AIRS) to medical decision support systems. We have used the heart disease and hepatitis disease datasets taken from UCI machine learning database as medical dataset. Artificial immune recognition system has shown an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, and liver disorders classification. The proposed approach consists of three stages. In the first stage, the dimensions of heart disease and hepatitis disease datasets are reduced to 9 from 13 and 19 in the feature selection (FS) sub-program by means of C4.5 decision tree algorithm (CBA program), respectively. In the second stage, heart disease and hepatitis disease datasets are normalized in the range of 0,1] and are weighted via fuzzy weighted pre-processing. In the third stage, weighted input values obtained from fuzzy weighted pre-processing are classified using AIRS classifier system. The obtained classification accuracies of our system are 92.59% and 81.82% using 50-50% training-test split for heart disease and hepatitis disease datasets, respectively. With these results, the proposed method can be used in medical decision support systems.