Parametric Analysis of Heart Attack Prediction Using Machine Learning Techniques

Heart diseases have become one of the most dreadful diseases nowadays around the world. It is a major problem in all groups of ages. Heart diseases are not easy to predictable. However, with the evolving technology, it is not really difficult to accomplish this task of prediction. The key point here is to evaluate datasets containing patient's health factors and analyze the data using some machine learning techniques after which the results can be used for prediction and prevention of this disease. Machine learning techniques are very popular nowadays due to the capability of their learning from massive amounts of data. Also, it is important to ensure that the patient's data remains private to the third parties. In this research paper, our proposed methodology considers a dataset with each row containing the patient's health monitoring factors. We test few well known machine learning techniques for the prediction of heart attacks. We also present a comparison of used machine learning algorithms over different evaluation metrics.