A Consistent Deep Neural Network to Diagnose Heart Disease

• Main Authors: Babu, M. Vijay Sekhar, Jha, Anuradha, Soni, Mukesh, Shafiq, Shafiqul Islam, Gupta, Sonali, Singh, Pavitar Parkash
• Format: Conference Proceeding
• Language: English
• Subjects: Automated Decision Support System; Cardiac disease; Classifier; Correlation; Decision support systems; Deep learning; Deep Neural Network; Feature extraction; Heart; Intelligent System; Machine Learning; Neural networks
• ISSN: 2767-7788
• DOI: 10.1109/ICICT57646.2023.10133948

Modern life is permeated by machine learning's effects and the health care industry is no exception. It is possible to identify cardiac disease with the help of a decision support system that is based on machine learning by analyzing a patient's clinical data. This research study explores different machine learning methods and apply them to solve the challenges in cardiac disease detection, with particular attention to coronary artery disease. While developing a model, feature selection is crucial since reducing the total number of features helps keep the system simple and efficient. To tackle this problem, three hybrid feature selection algorithms-GARFE (genetic algorithm recursive feature elimination), CFGA (correlation feature selection genetic algorithm), and HPCBE (hybrid Pearson correlation with backward elimination)-have been proposed, and empirical evidence suggests that they outperform more traditional feature selection approaches. This research study has developed a consistent deep neural network model for cardiac disease detection and achieves 95.31% accuracy by using the Cleveland dataset. Lastly, a graphical user interface is developed to make the system more approachable, aiding medical practitioners in the screening and detection of cardiac diseases.