Title:An Efficient Attribute Reduction and Fuzzy Logic Classifier for Heart Disease and Diabetes Prediction
VOLUME: 14 ISSUE: 1
Author(s):G. Thippa Reddy* and Xiao-Zhi Gao
Affiliation:School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu-632 014, Lappeenranta University of Technology
Keywords:Disease prediction, rough sets theory (RS), attribute reduction, fuzzy logic system (FLS), Hybrid BAT and Firefly
(HFBAT) optimization algorithm.
Abstract:Over the past decade Heart and diabetes disease prediction are major research works in
the past decade. For prediction of the Heart and Diabetes diseases, a model using an approach based
on rough sets for reducing the attributes and for classification, fuzzy logic system is proposed in this
paper. The overall process of prediction is split into two main steps, 1) Using rough set theory and
hybrid firefly and BAT algorithms, feature reduction is done 2) Fuzzy logic system classifies the
disease datasets. Reduction of attributes is carried out by rough sets and Hybrid BAT and Firefly optimization
algorithm. Then the classification of datasets is carried out by the fuzzy system which is
based on the membership function and fuzzy rules. The experimentation is performed on several
heart disease datasets available in UCI Machine learning repository like datasets of Hungarian,
Cleveland, and Switzerland and diabetes dataset collected from a hospital in India. The experimentation
results show that the proposed prediction algorithm outperforms existing approaches by achieving
better accuracy, specificity, and sensitivity.
