Prediction of Early Heart Attack Possibility Using Machine Learning

The most vital or important organ in our body is the heart. Over the recent decades, cardiac diseases has been the primary cause of mortality worldwide. Our heart is employed to regulate and sustain blood flow. A data-driven prediction model that takes into consideration the risk factors for heart disease might be quite useful in the healthcare industry to reach an early diagnosis of heart disease. Clinical practitioners and academics are very interested in developing a reliable method for predicting cardiac disease. The research paper’s main focus is on those who are more prone to develop heart disease given certain medical criteria, therefore improves medical treatment and lowers costs. We have employed some of the machine learning to compare the accuracy of several machine learning techniques, and it is observed that Random Forest classifier outperformed with the accuracy of 87.9% comparing to other machine learning algorithms.