Novel and Efficient Hybrid Model for Classification of Heart Disease | Chapter 3 | Current Approaches in Science and Technology Research Vol. 9

 Propose an effective cardiac disease categorization method that can predict disease early on and cut death rates. The study used a hybrid intelligence model of Genetic Algorithm (GA) and Support Vector Machine (SVM) for prediction, and the Cleveland dataset from the UCI machine learning library was used. SVM and GA were used to predict coronary artery disease by maximising the hyper parameters of SVM: ‘C' and ‘gamma.' Implementing meta-heuristics improved the performance of heart disease classification and resulted in a 91 percent accuracy when compared to SVM without GA. In terms of accuracy, a method of optimising SVM parameters using GA outperforms SVM and SVM with k-cross validation for predicting heart disorders. It points in the direction of making machine learning algorithms more efficient.

Author (S) Details

Mittal Desai
CMPICA, Charotar Univerity of Science and Technology (CHARUSAT), Gujarat, India.

Atul Patel
CMPICA, Charotar Univerity of Science and Technology (CHARUSAT), Gujarat, India.

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