• Malkari Bhargav
• J.Raghunath

In this modern days, there are many changes occur in our daily life. It will mainly impacting on health system. As a result of this various changes, health diseases are rapidly increasing in our day to day life. Here is some of diseases are more affected in our life. Such as cardiovascular Diseases, Stress Depression, Cancer and many more diseases are present in our today’s life. Mainly, cardiovascular disease is more commonly affected in our life. It will affect in any age group persons. The main cause of this cardiovascular disease is changes in the Blood Pressure, Cholesterol, increasing Heartbeat etc.. It may lead to risk for life and death also. Coronary Heart disease is caused by fatty plaque deposits on narrowed arteries walls supply to the heart and it will reduce the flowing of blood in heart. The main aim of this project is to predict the heart disease with machine learning algorithms and diagnose in early stages. In this research, we are implementing different machine learning algorithms with UCI dataset to find the best accuracy in different algorithms. Then i got best accuracy in Artificial Neural Network. So ANN classification algorithm is used to know the possibilities of getting heart disease and diagnose in initial stage.

Support Vector Machine, Decision Tree, Logistic Regression, Random Forest, KNN, Artificial Neural Network, Machine Learning, Keras, TensorFlow, Cardiovascular Disease Prediction

"A Study on Risk Prediction of Cardiovascular Disease Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 8, page no.683-688, August 2020, Available :http://www.jetir.org/papers/JETIR2008396.pdf

"A Study on Risk Prediction of Cardiovascular Disease Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 8, page no. pp683-688, August 2020, Available at : http://www.jetir.org/papers/JETIR2008396.pdf