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Heart disease classification using optimized Machine learning algorithms

Early detection of heart disease is exceptionally critical to saving the lives of human beings. Heart attack is one of the primary causes of high death rates throughout the world, due to the lack of human and logistical resources. In addition to the high costs of diagnosing heart diseases, which play a key role in the healthcare sector, this model is suggested. In cardiology, patient data plays an essential role in the healthcare system. This paper presents a proposed model that aims to identify the optimal machine learning algorithm that can accurately predict heart attacks in the early stages. Machine learning concepts are used for training and testing the model based on the patient's data for effective decision-making. The proposed model consists of three stages, the first stage is patient data collection and processing, and the second stage is data training and testing using machine learning algorithms (Random Forest, Support Vector Machines, K-Nearest Neighbor, and Decision Tree) that show The best classification (94.958 percent) is with the Random Forest algorithm and the third stage is optimizing the classification results using one of the hyperparameters optimization techniques, random search, that shows The best accuracy was (95.4 percent) obtained also with RF.