• Ms Sarika Sanjay Wabale
• Prof. Dr. N.R. Wankhede

Cardiovascular diseases (CVDs), which are diseases of the heart, are the main cause of the large number of fatalities that have occurred over the course of the most recent few years and have become the most dangerous disease in India and throughout the entire world. In this approach, there might be a need for a precise, workable, and reliable tool to study these illnesses in time for effective therapy. Numerous clinical datasets were used in conjunction with machine learning methods and techniques to conduct extensive and complex information research. In recent years, several analysts have used a variety of methodologies to provide the health care industry and internal specialists with the prediction of heart-related disorders. This study presents a survey of many models that are entirely based on such algorithms and techniques and examines their effectiveness. Models using supervised learning techniques, such as Support Vector Machines (SVM), Logistics Regression, Artificial Neural Network and random forest ensemble models are incredibly distinctive among the many researchers.

Cardiovascular, datasets, Supervised learning algorithms, Support Vector Machines, Logistics Regression, Artificial Neural Network

"Heart Disease Prediction Using Machine Learning and Risk Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.b343-b351, March-2023, Available :http://www.jetir.org/papers/JETIR2303142.pdf

"Heart Disease Prediction Using Machine Learning and Risk Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppb343-b351, March-2023, Available at : http://www.jetir.org/papers/JETIR2303142.pdf