Heart Disease Prediction: Optimizing Accuracy through Hyperparameter Tuning.

Machine learning (ML) and Artificial intelligence (AI) play pivotal roles in diverse domains, particularly in handling the vast influx of data in recent times. These technologies offer potential for more accurate and expedited decision-making, particularly in predicting diseases. A significant health concern is cardiovascular disease, a prominent factor contributing to mortality in recent times. Detecting heart disease in its early stages can significantly impact survival rates and quality of life. Prompt diagnosis is crucial, given the complexity and varied causes associated with this ailment. The inquiry primarily focuses on improving the accuracy of disease prognosis through the application of Machine Learning approaches like Support Vector Machine, Random Forest and XGBoost. A dedicated effort was made to refine the accuracy of SVM by fine-tuning hyperparameters through GridSearchCV. The comprehensive evaluation of these algorithms revealed that SVM achieved an accuracy of 80.22%, XGBoost exhibited 75.82% accuracy, while Random Forest notably outperformed both, boasting an accuracy of 85.71%. These results underscore the strong predictive abilities of Random Forest in detecting heart disease, outperforming the efficacy of the other models. The study's results highlight the capability of machine learning, particularly Random Forest, in forecasting heart disease. Collaborative efforts with medical professionals and ongoing research aim to further refine these models, pushing accuracy rates closer to 100%. This advancement could mark a significant breakthrough in machine learning-driven disease prediction algorithms. In summary, this study showcases the prognostic capacities of SVM, Random Forest, and XGBoost in heart disease prognosis. Emphasizing the superior performance of Random Forest, the findings advocate for its adoption in improving disease diagnostics and intervention strategies. [ABSTRACT FROM AUTHOR]