• Arpita Sharma
• Prof. Rupali Chaure
• Dr. Ritu Shrivastava

Cardiac disease is a major global health problem in modern medicine. The twenty-firstcentury adage consummate proliferation in life expectancy and a significant transference in the causes of heart disease bereavement throughout the world. The criticality of cardiac diseases are more crucial and can even lead to vulnerable consequences if it is not detected at an earlier stage. The techniques such as electronic health records, body area networks are emerged to continuously monitor and diagnose patient’s health conditions through the projection of medical sensors and wearable devices across human bodies. Since the data generated from the body area networks are continuous and tremendous in volume, the machine learning techniques are used for efficient health data classification processes. However, health data classification is the most challenging process as it needs to be executed accurately with an earlier prediction of heart diseases. Machine learning (ML) provides a reliable and excellent support for prediction of a heart diesease with correct case of training and testing. Diagnosis of heart mellitus desires great support of machine learning classifiers to detect diabetes disease in early stage, since it cannot be cured which brings great complication to our health system.

Cardiac Disease, Heart Disease, Gradient Boosting, ML

"Risk Prediction of Heart Disease using Gradient Boosting based Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 3, page no.c538-c543, March-2022, Available :http://www.jetir.org/papers/JETIR2203273.pdf

"Risk Prediction of Heart Disease using Gradient Boosting based Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 3, page no. ppc538-c543, March-2022, Available at : http://www.jetir.org/papers/JETIR2203273.pdf