Heart Attack Probability Analysis Using Machine Learning

Heart Attack is one of the most common diseases observed in people of middle age as well as old age in the present day scenario. This may be due to unhealthy food habits and negligence of health in most people. Detecting the risk of heart attack and taking timely medication, can prevent serious illness. In this paper we explain about the different machine learning approaches and techniques used for predicting the probability of heart-attack risk. Different models are applied for heart-attack risk prediction. The probability of heart attack risk is displayed through a website. If a person is found having risk, suitable precautions are displayed under the guidance of the cardiologist. The proposed work analyses whether the person has a normal range of values for some highly contributing attributes which lead to heart attack like Cholesterol, Blood pressure, Blood sugar. The proposed work has better results compared to the previous work in terms of accuracy of prediction with highest value of accuracy as 85.7% for SVM model.