Machine Learning based selection of Myocardial Complications to Predict Heart Attack

Myocardial Infarction term used for heart attack is a condition in which the functioning of heart becomes abnormal due to various factors like blockage of arteries and veins which in turn results in heart attack. Machine Learning became the most suitable approach to develop the models for prediction in various health care sectors. Apart from myocardial infarction prediction ML is useful in predicting various diseases. Predicting heart disease could be risky if the accurate results are not fetched the patient could die. The analytical tools help in predicting various diseases with the help of existing data of health reports. The motive of this research is to predict heart attack with the help of 12 complications that could possibly happen after the first heart attack. The machine learning algorithms used are Support-Vector Machine (SVM), Logistic-Regression which are used in deploying a model to predict heart attack. This study intends to ideate the prediction for chances of occurrence of a heart attack inside the sufferers. The prime objective of this study is for prediction of the myocardial infarction complications in a patient using UCI machine learning repository and various algorithms.