Web Based Machine Learning Application for Heart Disease Prediction

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Lokesh M. Giripunje, Tejas P. Sonar, Rohit S. Mali, Jayant C. Modhave, Mahesh B. Gaikwad


In the healthcare sector, doctors are facing many issues for predicting diseases. Coronary heart disease is one of them and it should be managed precisely and efficiently. Hospitals & clinics are offering costly therapies and operations to treat heart disease. Therefore, prediction of heart diseases at an early stage will give insight to doctors. Optimized solution for this problem can be solved by machine learning techniques. Machine learning in healthcare helps to analyze the tons of data & provide precise outcomes. In this work we have referred to some previous papers and improved their accuracy. And then compared the accuracies of machine learning algorithms like Decision tree, Random forest, KNN and SVM.

               Heart disease prediction is considered a classification-based problem. So, the Random Forest classifier is the most accurate algorithm which gives accuracy of 97% and it is a suitable model for further processes. In addition, deployment of machine learning models using web applications is done with the help of flask framework, HTML, GitHub and Heroku server. Under deployment, the webpage will take input attributes from the user and give the output regarding the patient’s condition with probability in percentage for occurrence of coronary heart disease in upcoming ten years.

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