Published Paper Details:

Heart disease is becoming one of the most significant reasons for mortality and almost a lot of human beings are suffering from this problem. As we all know it is not so easy to predict the heart disease prior without having very good clinical knowledge. In current days all the predictions are done manually with error rate to find out the abnormalities. In general the manual prediction always makes a lot of errors and a lot of effort is required to process the manual records and hence this motivated me to propose this article in which heart disease prediction can be done by using several Machine Learning algorithms. In general, machine learning is a domain which greatly increases its capabilities in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. In this proposed work, we propose an ensemble model by collecting several ML classification models in one location and then test which model gives more accuracy in the prediction of heart diseases. This proposed work is trained by using several ML algorithms and then checking the following factors such as accuracy, precision, recall and F1-Score.By conducting various experiments on several ML Algorithms by taking UCI dataset, we finally check which algorithm fits best for efficient heart disease prediction.