Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of biggest medical difficulties in recent years. Coronary (CHD) a kind heart and blood vascular disease. Predicting this sort cardiac illness leads to more precise decisions for disorders. Implementing Grid Search Optimization (GSO) machine training models therefore useful way forecast sickness as soon possible. The state-of-the-art work tuning hyperparameter together with selection feature by utilizing model search minimize false-negative rate. Three cross-validation approach do required task. Feature Selection based on use statistical correlation matrices multivariate analysis. For Random models, extensive comparison findings are produced retrieval, F1 score, precision measurements. evaluated using metrics kappa statistics that illustrate three models’ comparability. study effort focuses optimizing function selection, tweaking hyperparameters improve accuracy prediction examining Framingham datasets random forestry classification. Tuning grid thus decreases erroneous rate achieves global optimization.