A method for improving prediction of human heart disease using machine learning algorithms

A great diversity comes in the field of medical sciences because of computing capabilities and improvements in techniques, especially in the identification of human heart diseases. Nowadays, it is one of the world’s most dangerous human heart diseases and has very serious effects the human life. Accurate and timely identification of human heart disease can be very helpful in preventing heart failure in its early stages and will improve the patient’s survival. Manual approaches for the identification of heart disease are biased and prone to interexaminer variability. In this regard, machine learning algorithms are efficient and reliable sources to detect and categorize persons suffering from heart disease and those who are healthy. According to the recommended study, we identified and predicted human heart disease using a variety of machine learning algorithms and used the heart disease dataset to evaluate its performance using different metrics for evaluation, such as sensitivity, specificity, F-measure, and classification accuracy. For this purpose, we used nine classifiers of machine learning to the final dataset before and after the hyperparameter tuning of the machine learning classifiers, such as AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB. Furthermore, we check their accuracy on the standard heart disease dataset by performing certain preprocessing, standardization of dataset, and hyperparameter tuning. Additionally, to train and validate the machine learning algorithms, we deployed the standard K-fold cross-validation technique. Finally, the experimental result indicated that the accuracy of the prediction classifiers with hyperparameter tuning improved and achieved notable results with data standardization and the hyperparameter tuning of the machine learning classifiers.