SUPER LEARNER MODEL IN PREDICTION OF HEART ATTACK BASED ON CARDIAC BIOMARKERS

Abstract

Unstable angina and/or a heart attack is caused when restricted flow of blood to the heart occurs due to the narrowed or blocked coronary arteries. On observing Electro cardiogram (ECG), ST segment Elevation Myocardial Infarction (STEMI) can be diagnosed but ECG might not show variation for Non-ST Segment Elevation Myocardial Infarction (NSTEMI). So, cardiac biomarkers could be tested in patients presenting chest pain to confirm whether heart attack or Acute Myocardial Infarction (AMI) is onset or not. Myoglobin, Troponin-I and CK-MB are sensitive biomarkers for diagnosing heart attack/ AMI within specific time frames. In this work, a novel real dataset from a hospital comprising cardiac biomarkers’ values of patients was taken and Machine Learning (ML) classifiers namely Support Vector Machine, Logistic Regression (LR), XGBoost (XGB), CatBoost, Random Forest (RF), Decision Tree Classifier, Gaussian Naïve Bayes (GNB), Majority Vote Ensemble Classifier comprising of LR, XGB, GNB, RF were applied on the dataset. Then a Super Learner was designed by taking a novel combination of these classifiers. The comparison of these classifiers resulted in Super Learner outperforming the other ML classifiers. Subsequently, a graphical user interface prediction tool using the Super Learner model was designed which would guide those who have chest pain due to AMI, to undergo emergency medical care and thereby save lives.