A Comprehensive Analysis on Risk Prediction of Acute Coronary Syndrome Using Machine Learning Approaches

Abstract

Acute Coronary Syndrome (ACS) is liable for the sudden death. The originator of tachycardia is drug addiction, hyperpiesia polygenic disorder, lipidemia. From the healthcare unit, ACS patients dataset has been collected. By preprocessing the information the chances of the exigency of tachycardia by possessing machine learning (ML) approaches are analyzed. The proficiency of ML techniques for prediction is authentic than any other traditional systems. The central scheme of this analysis is to anticipate the significant contingency of tachycardia. Neural Network, SVM, AdaBoost, Bagging, K-NN, Random Forest approaches are used as long as anticipating the betrayal of ACS. The high-grade exactness with AdaBoost and Bagging are 75.49% and 76.28%. The precision and recall for AdaBoost are 0.741; 0.75 and 0.755; 0.763 for Bagging techniques respectively.