Optimization of Heart Disease Prediction using Supervised learning with Hyperparameter Optimization Methods

Farij O. Ehtiba Ehtiba; Suhaila F. Elfaitouri; Haitham S.  Ben Abdelmula; Ali Elghirani

المؤلفون

Farij O. Ehtiba Ehtiba Libyan Academy for Postgraduate Studies
Suhaila F. Elfaitouri Libyan Academy for Postgraduate Studies, School of Basic Sciences, Department of Computer Science -Misurata, Libya
Haitham S. Ben Abdelmula Computer Networks Department, College of Computer Technology – Zawia, Zawia, Libya
Ali Elghirani Faculty of Information Technology, Libyan International Medical University, Benghazi – Libya

الكلمات المفتاحية:

machine Learning، features selection، cardiovascular diseases، Grid search

الملخص

Coronary artery disease prediction is a challenging task in healthcare due to the increasing mortality rate associated with heart disease globally. Various machine learning techniques, such as logistic regression, support vector machine, K-nearest neighbors, and random forests, have been employed to create predictive models for early detection. By fine-tuning the hyperparameters, the accuracy of these models can be significantly enhanced. The study outcomes demonstrated different levels of accuracy for each algorithm, with logistic regression achieving up to 86.13% accuracy, support vector machine up to 85.71%, Random Forest exhibiting superior performance with 91.60% accuracy, and K-Nearest Neighbors emerging as the top performer with 92.44% accuracy. This research underscores the potential of utilizing relatively simple supervised machine learning algorithms to predict heart disease with exceptional accuracy, highlighting their significant utility in healthcare.

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