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

  • G. Thippa Reddy
  • M. Praveen Kumar Reddy
  • Kuruva Lakshmanna
  • Dharmendra Singh Rajput
  • Rajesh Kaluri
  • Gautam SrivastavaEmail author
Special Issue
  • 14 Downloads

Abstract

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.

Keywords

Disease classification Adaptive genetic algorithm Rough set theory Feature reduction Membership function 

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Vellore Institute of TechnologyVelloreIndia
  2. 2.Department of Mathematics and Computer ScienceBrandon UniversityBrandonCanada
  3. 3.Research Center for Interneural ComputingChina Medical UniversityTaichungTaiwan, ROC

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