Ensemble Approaches Can Aid In The Early Detection Of Coronary Heart Disease

Abstract

Due to the fact that heart disease is the leading cause of death worldwide, it is critical to recognize it early. Artificial intelligence (AI) is a relatively new technology that is being heavily applied in a variety of fields, including biomedical care and disease prediction. Deep learning and machine learning are two examples of relatively new technologies that are being heavily applied in the fields of biomedicine, healthcare, and the early detection of disease. The goal of this study is to see if human coronary heart disease risk factors can be predicted using risk variables (CHD). In order to evaluate the effectiveness of prediction techniques like K-Nearest Neighbors, Binary Logistic Classification, and Naive Bayes, it is required to measure the accuracy and recall of each prediction method (BLC). Bundling and boosting are examples of ensemble modelling techniques are comparable to these methods of predicting the future. For the purpose of determining whether or not ensemble techniques can improve the accuracy of coronary heart disease prediction, a comparative analytical method was adopted. These patient data records for coronary heart disease total approximately 70,000 records and serve as a testing ground for the modeling methodologies that are currently being researched and developed. There is a 1.96 percent increase in accuracy between bagged models and their conventional equivalents. The improved models outperformed all other models by a wide margin, with an average AUC of 0.73. A combined accuracy of 75.1 percent was achieved by using the SVM, KNN, and random forest classifiers, which were regarded to be the most accurate. Utilizing data analysis and K-Fold cross-validation, the performance of the tested models was assessed...