A Novel Web-Based Multi-Class Heart Disease Prediction Using Machine Learning Algorithms

In the present scenario, heart disease impacts human life very inadequately and raises the cause of death in the world. In order to prevent heart failure, an early precise and on-time diagnosis is very significant. Through the conventional medical record, heart disease diagnosis has not been considered reliable in many aspects. In this regard, the authors developed a novel medical diagnosis system using machine learning (ML) algorithms. The optimal set of features is considered to enrich the proposed heart disease multi-class classification superiority by utilizing the different feature selection methods. The authors utilized Random Forest (RF), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Extreme Gradient (XGBoost), and ensemble learning classifiers (Stacking and Voting) to perform the multi-class classification. To determine and investigate the findings realized by ML algorithms, the performance measures such as receiver optimistic curve (ROC), accuracy, precision, f-score, and recall are considered and observed. In addition, the FLASK framework was implemented and deployed for the application programmable interface (API) and web page. The results reveal that the Stacking ensemble classifier accomplished exceptional accuracy and the ROC score of 93.5% and 0.99%, respectively. The suggested medical diagnosis system will assist doctors/hospitals in predicting heart disease risk aspects easily.