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.
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