Improvement in the performance of deep learning based on CNN to classify the heart sound by evaluating hyper-parameters

Abstract

The effective prediction of heart disorders is crucial for timely intervention and treatment before a cardiac event occurs. While various machine learning models have been developed for this purpose, many struggle to handle high-dimensional data effectively, limiting their performance. In this work, efforts have been made to enhance the performance and computational efficiency of deep learning classifiers using hyperparameters. The study utilized heart sound data from normal and diseased patients obtained from standard online repositories. The hyperparameter tuned modified CNN-based Inception Network model achieved an accuracy of 99.65% ± 0.23% on the test dataset, along with a sensitivity of 98.8% ± 0.12% and specificity of 98.2% ± 0.15%. Thus the hyperparameter-tuned CNN-based Inception Network model outperformed its counterparts, making it the most effective model for diagnosing heart disorders.