Prediction of Heart Diseases Using Machine Learning Algorithm - Swati Jadhav, Mahesh Almale, Yash Bhirud, Kapil Kawathekar and Prashant Teli (International Journal of Advanced Science and Technology, 2020)
Medical science is growing so fast nowadays. As there are more number of people, disease related to each and every individual along with its own symptoms associated with it is a major concern. It is very difficult to predict the result of particular disease because of huge data generation of large number of people. As medical field is having so much amount of data but not able to generate effective output because of unavailability of disease prediction system. These disease prediction systems help to extract the useful information from this huge data and generate the result. Diseases related to heart like Silent Ischemia and Coronary Artery Disease are very difficult to predict because of lot of patients and huge data generation along with symptoms. So this system is able to produce the exact result of having those two heart disease with maximum accuracy. The temporary data set has been collected from Kaggle website to perform operations on it and to predict the output. With the help of machine learning algorithms and applying association rules on the data set, the accuracy has been predicted. In order to predict the correct output SVM and Decision Tree algorithm is chosen because of high F1 score and applied on the data set with various input parameters like age, sex, gender, blood pressure etc. The main motive is to guess that the patient is suffering or not suffering from the disease with maximum accuracy with the help of these algorithms.