Heart disease detection and classification using grid search with random forest

Abstract

Cardiovascular disease (CVD) is basically stated as heart disease, is a significant impact of mortality rate in worldwide. Diagnosing heart disease is challenging because of the complexity of patient data, which establishes multiple categories of the disease and also irrelevant features, making it difficult to achieve classification accuracy. This research proposed a grid search with random forest (GS-RF) approach, which effectively identifies heart disease and significantly enhances classification accuracy by fine tuning the random forest (RF) approach. It optimizes key hyperparameters like number of trees and greater number of features, improving model performance. The chaotic maps-based dwarf mongoose optimization (CMDMO) is used for feature selection, which efficiently selects the relevant feature and prevents the algorithm from getting trapped in local minima. The classification using grid search’s effectiveness ensures that resources are spent on finding the best model rather than performing random, less efficient tuning. The proposed GS-RF model demonstrates high classification performance, achieving 99.43% accuracy on Cleveland dataset, while also attaining 99.10% accuracy on Statlog dataset, thereby confirming its robustness and effectiveness across different datasets. The proposed approach is evaluated in comparison with existing classification techniques, such as support vector machine (SVM), to demonstrate its greater effectiveness with respect to accuracy.