Optimization of Heart Disease Prediction using Supervised learning with Hyperparameter Optimization Methods (Journal of Academic Research, 2024)
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.