TO STUDY DIFFERENT MACHINE LEARNING ALGORITHMS FOR PREDICTION OF HEART DISEASE - Dhanashri Mahesh Raut1, Trupti Madan Kulkarni 2 (International Journal of Innovations in Engineering Research and Technology, 2022)
Heart disease causes a significant mortality rate around the world, and it has become a health threat for many people. Early prediction of heart disease may save many lives; detecting cardiovascular diseases like heart attacks, coronary artery diseases etc., is a critical challenge for regular clinical data analysis. According to the World Health Organization report published in 2019, around 17.9 million people die every year worldwide due to heart disease [1]. There are various types of heart diseases such as coronary artery disease, congenital heart disease, arrhythmia, etc. The patient suffering from heart disease has various symptoms such as chest pain, dizzy sensations, and deep sweating. Smoking, high blood pressure, diabetes, obesity,etc. are the main reasons behind heart disease.In India, more than 17 Lakh people die every year due to heart diseases and by 2030, the figure is expected to increase with 2.3 crore deaths. Invasive methods of predicting the disease are expensive and painful. Therefore, there is a need for a technique that can predict heart disease in a non-invasive manner at less cost.Since the prediction of heart disease in people is very important, a method should be used in the right prediction of heart diseases that have the least errors in heart disease prediction.Hence to overcome & reduce the chances of death occurs due to heart disease, machine learning comes in role to predict the chances of upcoming heart disease according to the health conditions as well as previous medical conditions of the patient.Machine learning has rapidly developed within the last years and its extension into medical sciences offers the potential to revolutionize the way in which complex diagnostic and prognostic estimations at the level of the individual patient are performed. Machine Learning algorithms such as Random Forest, Support Vector Machine (SVM), Naive Bayes and Decision tree have been used for the development of model.