Data Analysis on Myocardial Infarction with the help of Machine Learning Algorithms considering Distinctive or Non-Distinctive Features

The target of this research is to analyze the accuracy level for predicting Myocardial Infarction with the help of Machine Learning Algorithms. The rate of heart attacks in Bangladesh is increasing immensely day by day. It is a kind of disease (known as coronary artery disease) that occur when there is a loss of blood supply to the heart. In medical terms heart attack is commonly known as Myocardial Infarction. This is very important to find a way to predict the chances of occurrence of Myocardial Infarction beforehand to reduce the rate before it turns out to be a major issue. Hence our research is based on predicting the chances of occurring Myocardial Infarction so that people can take precautions and take measures to prevent it. This research includes a collection of data and the classification of data using Machine Learning Algorithms. We have collected 345 instances along with 26 attributes. This data have been collected from patients suffering from myocardial infarction along with other symptoms. The class attribute contains three types of category which are Distinctive, Non-Distinctive and Both. The training of the dataset have been done with K-Fold Cross Validation Technique and specifically three Machine Learning algorithms have been used which are Bagging, Logistic Regression and Random Forest. And, this research could be able to show accuracy for the above mentioned machine learning algorithms are 93.913%, 93.6323% and 91.0145% respectively.