• Mogali . V.P.P.Akshita
• Dr. S. Rao Chintalapudi

In today’s era deaths due to heart disease has become a major issue approximately one person dies per minute due to heart disease. Accurate and on time diagnosis of heart disease is important for coronary failure prevention and treatment. In order to lower the number of deaths from heart diseases, there has to be a fast and efficient detection technique. This paper presents a model for detecting heart disease using machine learning algorithms. The methodology adopted is such a way that a Heart Dataset was trained using four different machine learning algorithms K-Nearest Neighbor, Support Vector Machine, Decision Tree and Random Forest. Hence, it is a binary classification problem, these models will predict the heart disease based on the attributes provided. The performance of these four models is measured in terms of accuracy. The K-Nearest Neighbor model is producing good results than the three models.

Machine Learning, Cardiovascular disease, K-Nearest Neighbors, Support Vector machine, Decision Tree, Random Forest.

"A Machine Learning based framework for Heart Disease Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 2, page no.346-350, February-2021, Available :http://www.jetir.org/papers/JETIR2102037.pdf

"A Machine Learning based framework for Heart Disease Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 2, page no. pp346-350, February-2021, Available at : http://www.jetir.org/papers/JETIR2102037.pdf