ANT_FDCSM: A novel fuzzy rule miner derived from ant colony meta-heuristic for diagnosis of diabetic patients

Abstract

The ever increasing advances in the field of biotechnology and health information sciences have led to large electronic health records (EHRs), which in turn contains important genetic and clinical information. Machine learning and data mining techniques are playing vital and indispensible efforts to intelligently convert available data into useful information for effective medical diagnosis. But, designing effective prediction and diagnosis techniques for diabetes mellitus (DM) are getting more attention than ever before. Thus, a novel fuzzy rule miner (ANT_FDCSM) derived from ant colony meta-heuristic for diagnosis of diabetic patients has been proposed in this paper. A few important improvements have been suggested to improve the performance of traditional ant colony optimization induced decision tree classifier. The first improvement is done to optimize search space of construction graph by employing a novel approach for optimal split point selection. Secondly, to compute heuristic information, a hybrid node split measure (SW_FDCSM) is presented. SW_FDCSM is a combination of attribute significance weight (SW) with a new fuzzy variant (Fuzzy_DCSM) of famous distinct class split measure (DCSM). The improvements have been proposed to generate comprehensive rule set while maintaining good accuracy, sensitivity and specificity count. A 10 fold cross validation (10-FNo) is applied on Pima Indian Diabetes (PID) data set to validate the performance of the proposed fuzzy rule miner (ANT_FDCSM).