Feature selection model for healthcare analysis and classification using classifier ensemble technique

The diagnosis of heart disease is found to be a serious concern, so the diagnosis has to be done remotely and regularly to take the prior action. In the present world finding the prevalence of heart disease has become a key research area for the researchers and many models have been proposed in the recent year. The optimization algorithm plays a vital role in heart disease diagnosis with high accuracy. Important goal of this work is to develop a hybrid GA-ABC which represents a genetic based artificial bee colony algorithm for feature-selection and classification using classifier ensemble techniques. The ensemble classifier consists of four algorithms like support vector machine, random forest, Naive Bayes, and decision tree. From the obtained results, the proposed model GA-ABC-EL shows increase in the classification accuracy by obtaining more than 90% when compared to the other feature selection methods.

Automated summary

The study developed a hybrid model based on genetic algorithm and artificial bee colony algorithm for feature selection and classification of heart disease, achieving over 90% classification accuracy.