Grid Search based Optimum Feature Selection by Tuning hyperparameters for Heart Disease Diagnosis in Machine learning

Abstract

Background: Heart disease prediction model helps physicians to identify patients who are at high risk of developing heart disease and target prevention strategies accordingly. These models use patient demographics, medical history, lifecycle factors, and clinical measurements to calculate the risk of heart disease within a certain time frame. In identifying important features of heart disease, a popular approach is using Machine learning (ML) models. ML models can analyse a large amount of data and find patterns that are difficult for humans to detect. Methods: In this proposed work, Random Forest classifier is used to identify the most important features that contribute to heart disease and increase the prediction accuracy of the model by tuning the hyperparameters using grid search approach. Results: The proposed system was evaluated and compared in terms of accuracy, error rate and recall with the traditional system. As the traditional system achieved accuracies between 81.97% and 90.16%., the proposed hyperparameter tuning model achieved accuracies in the range increased between 84.22% and 96.53%. Conclusion: These evaluations demonstrated that the proposed prediction approach is capable of achieving more accurate results compared with the traditional approach in predicting heart disease by finding optimum features.