Acute Myocardial Infarction: Prediction and Patient Assessment through Different ML Techniques
Keywords:
Acute Myocardial Infarction, Machine Learning, Ensemble Classifiers, Classification methods, Logistic Regression, Random Forest Classifier, SVMAbstract
Myocardial Infarction stands as a prevalent and severe ailment on a global scale. It ranks among the primary contributors to the world's highest mortality rates. Sometimes a Myocardial Infarction can show no symptoms at all. It is a disease that occurs when there is less supply of blood to the heart. In this research paper the main aim is to evaluate various techniques of Machine Learning to predict accurately the disease and the adverse effect of the risk factors. The different ML Techniques are applied on the dataset collected which includes 350 entries which includes some MI patients and some non-MI patients including men and women. The dataset is trained and then the Ensemble Classifiers are applied that increases prediction performance. The Ensemble Classifiers helps to improve gender specific prediction precision by merging classifier prediction.
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