Abstract
Coronary artery disease (CAD) is one of the most authentic cardio-vascular afflictions because it is an uncommonly overwhelming heart issue. The breakdown of coronary cardiovascular disease is one of the principal sources of death all over the world. Cardiovascular deterioration is a challenge, especially in youthful and rural countries where there is an absence of human -trained professionals. Since heart diseases happen without apparent signs, high-level detection is desirable. This paper proposed a robust and tuned ran-dom forest model using the randomized grid search technique to predict CAD. The proposed framework increases the ability of CAD predictions by tracking down risk pointers and learning the confusing joint efforts between them. Nowadays, the healthcare industry has a lot of data but needs to gain more knowledge. Our proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease (HD). We evaluated the proposed framework over two public databases, Cleveland and Framingham datasets. The datasets were pre-processed by using a cleaning technique, a normalization technique, and an outlier detection technique. Secondly, the principal component analysis (PCA) algorithm was utilized to lessen the feature dimensionality of the two datasets. Finally, we used a hyperparameter tuning technique, randomized grid search, to tune a random forest (RF) machine learning (ML) model. The randomized grid search selected the best parameters and got the ideal CAD analysis. The proposed framework was evaluated and compared with traditional classifiers. Our proposed framework's accuracy, sensitivity, precision, specificity, and f1 -score were 100%. The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.