Heart Disease Prediction Using Adaptive Infinite Feature Selection and Deep Neural Networks
Sudipta Modak, Esam Abdel‐Raheem, Luis Rueda
Abstract
Prediction of heart disease is one of the most important fields of study in modern science. By studying data such as cholesterol levels, blood sugar, and blood pressure, heart disease can be predicted. In recent years, several machine learning techniques have been used to aid in fast prediction by learning from the data. However, the prediction accuracy still remains low. This is due to lower number of records contained in the databases available. In this paper, we propose a new method of heart disease prediction using a modified variation of infinite feature selection and multilayer perceptron. The method shows a high accuracy of 87.70%, a high F1-score of 87.21%, a high sensitivity of 88.50%, a high specificity of 87.02%, and a high precision in prediction of 86.05%. on the Cleveland, Hungarian, Switzerland, Long Beach, and Statlog datasets. For evaluation purposes, we have combined all the datasets together and then divided the combined dataset into training and test samples with a 20 % percent of the samples allocated for testing.