A Model for Heart Disease Prediction Using Feature Selection with Deep Learning

The heart disease is considered as the most widespread disease. It is challenging for most of the physicians to diagnose at an early stage to avoid the risk of death rate. The main objective of this study involves the prediction of heart disease by using efficient techniques based on feature selection and classification. For feature selection, the enhanced genetic algorithm (GA) and particle swarm optimization (PSO) have been implemented. For classification, the recurrent neural network (RNN) and long short term memory (LSTM) has been implemented in this study. The data set used is the Cleveland heart disease data set available on UCI machine learning repository, and the performance of the proposed techniques has been evaluated by using various metrics like accuracy, precision, recall and f-measure. Finally, the results thus obtained have been compared with the existing models in terms of accuracy. It has been observed that LSTM when combined with PSO showed an accuracy of 93.5% whereas the best known existing model had an accuracy of 93.33%. Therefore, the proposed approach can be applied in the medical field for accurate heart disease prediction.