TITLE:
A Machine Learning-Based Web Application for Heart Disease Prediction
AUTHORS:
Jesse Gabriel
KEYWORDS:
Heart Disease, US Center for Disease Control and Prevention, Machine Learn-ing, Imbalanced Data, Web Application
JOURNAL NAME:
Intelligent Control and Automation,
Vol.15 No.1,
February
18,
2024
ABSTRACT: This work leveraged predictive modeling techniques in machine learning
(ML) to predict heart disease using a dataset sourced from the Center for
Disease Control and Prevention in the US. The dataset was preprocessed and used
to train five machine learning models: random forest, support vector machine,
logistic regression, extreme gradient boosting and light gradient boosting. The
goal was to use the best performing model to develop a web application capable of reliably predicting heart
disease based on user-provided data. The extreme gradient boosting
classifier provided the most reliable results with precision, recall and
F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and
21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease).
The model was further deployed as a web application.