• Suraj Raut
• Rishabh Magar
• Rohan Memane
• Prof. V. S. Rupnar

Heart disease is one of the major causes of death throughout the world. It cannot be easily predicted by the medical practitioners as it is a difficult task which demands expertise and higher knowledge for prediction. An automated system in medical diagnosis would enhance medical efficiency and also reduce costs. We will design a system that can efficiently discover the rules to predict the risk level of patients based on the given parameters about their health. The goal is to extract hidden patterns by applying data mining techniques, which are noteworthy to heart diseases and to predict the presence of heart disease in patients where the presence is valued on a scale. The prediction of heart disease requires a huge size of data which is too complex and massive to process and analyze by conventional techniques. Our objective is to find out the suitable machine learning technique that is computationally efficient as well as accurate for the prediction of heart disease. Data mining combines Statistical analysis machine learning and database technology to extract hidden patterns and relationships from large databases. The implementation of work is done on Cleveland heart diseases data set from the University of California Irvine (UCI) machine learning repository to test on different data mining techniques.

HEART DISEASE PREDICTION SYSTEM

"HEART DISEASE PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.2081-2085, June-2020, Available :http://www.jetir.org/papers/JETIR2006301.pdf

"HEART DISEASE PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp2081-2085, June-2020, Available at : http://www.jetir.org/papers/JETIR2006301.pdf