Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis

被引:265
作者
Reddy, G. Thippa [1 ]
Reddy, M. Praveen Kumar [1 ]
Lakshmanna, Kuruva [1 ]
Rajput, Dharmendra Singh [1 ]
Kaluri, Rajesh [1 ]
Srivastava, Gautam [2 ,3 ]
机构
[1] Vellore Inst Technol, Vellore, Tamil Nadu, India
[2] Brandon Univ, Dept Math & Comp Sci, Brandon, MB R7A 6A9, Canada
[3] China Med Univ, Res Ctr Interneural Comp, Taichung 40402, Taiwan
关键词
Disease classification; Adaptive genetic algorithm; Rough set theory; Feature reduction; Membership function; PREDICTION; SYSTEM;
D O I
10.1007/s12065-019-00327-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
For the past two decades, most of the people from developing countries are suffering from heart disease. Diagnosing these diseases at earlier stages helps patients reduce the risk of death and also in reducing the cost of treatment. The objective of adaptive genetic algorithm with fuzzy logic (AGAFL) model is to predict heart disease which will help medical practitioners in diagnosing heart disease at early stages. The model consists of the rough sets based heart disease feature selection module and the fuzzy rule based classification module. The generated rules from fuzzy classifiers are optimized by applying the adaptive genetic algorithm. First, important features which effect heart disease are selected by rough set theory. The second step predicts the heart disease using the hybrid AGAFL classifier. The experimentation is performed on the publicly available UCI heart disease datasets. Thorough experimental analysis shows that our approach has outperformed current existing methods.
引用
收藏
页码:185 / 196
页数:12
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