Network Biology, 2022, 12(2): 64-80
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A web-based heart disease prediction system using machine learning algorithms

Md. Mahbubur Rahman, Morshedur Rahman Rana, Md. Nur-A-Alam, Md. Saikat Islam Khan, Khandaker Mohammad Mohi Uddin
Department of Computer Science and Engineering, Dhaka International University, Dhaka-1205, Bangladesh

Received 23 February 2022;Accepted 11 March 2022;Published 1 June 2022

Disease diagnosis is the most critical task in the medical diagnosis system. At present, the biggest challenge is to predict heart disease very quickly; for that limitation, the number of dying people is increasing day by day. If a heart disease is diagnosed quickly, we can reduce the death rate indisputably. Thus, this research produces a manual and web-based automatic prediction system that can confer a conceptual report of clear warning of patient's heart condition. The proposed prediction system predicts heart disease using some health parameters. The system uses thirteen health parameters like age, sex, chest pain type, blood pressure, ECG, etc. Eight algorithms are used separately to diagnose heart disease accurately, namely KNN, XgBoost, Logistic Regression (LR), Support Vector Machine (SVM), Ada Boost, Decision tree (DT), Naive Bayes, and Random Forest (RF). Decision Tree and Random Forest provide better performance than others among all methods. This research also established a website to easily check their heart condition from home instantly. The system has used 1026 individual patients' data for training and testing. It achieves higher accuracy in the different algorithms such as DT (99%), RF (99%), XgBoost (95%), KNN (89%), SVM (85%), LR (85%), Ada Boost (83%) and Naive Bayes (82%). The experiment result provides a target value of 0 or 1 that refers to the patient's presence or absence of heart disease.

Keywords heart disease prediction;machine learning algorithms;web application;health parameters.

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