%0 Journal Article
%T A Machine Learning-Based Web Application for Heart Disease Prediction
%A Jesse Gabriel
%J Intelligent Control and Automation
%P 9-27
%@ 2153-0661
%D 2024
%I Scientific Research Publishing
%R 10.4236/ica.2024.151002
%X 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 <span>application capable of reliably predicting heart
disease based on user-provided </span>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.
%K Heart Disease
%K US Center for Disease Control and Prevention
%K Machine Learn-ing
%K Imbalanced Data
%K Web Application
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=131163