• Swarali Dabake
• Shraddha Patil
• Smriti Kadu
• Snehal Shinde

Recently, the diagnosis of heart disease has been one of the most complex tasks in the medical field. With the growing population, it becomes more and more difficult to diagnose and begin treatment early. Machine learning strategies have accelerated the health sector with more research. The machine learning model for heart rate predictor supports related parameters. The UCI heart prediction benchmark database will be used in this research project, which contains different parameters on heart condition. The machine learning algorithm which is a random forest will be used for the model event. The Random Forest algorithm will achieve much higher accuracy compared to other machine learning algorithms. This model will be useful to medical staff at their clinic as a decision support system.

Random Forest, Heart Disease Prediction.

"Heart Disease Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.e659-e664, April-2022, Available :http://www.jetir.org/papers/JETIR2204488.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.9, Issue 4, page no. ppe659-e664, April-2022, Available at : http://www.jetir.org/papers/JETIR2204488.pdf