• Priyanka parhad
• Dr.Vinod Wadne

Heart problem is common vital issues that are arising within the world these days. Find the disorder could be a crucial challenge within the space of clinical knowledge testing. With the vast amounts of data generated by healthcare organizations and hospitals, hybrid machine learning (ML) has shown to be an effective tool for making decisions and forecasts. These techniques have also been applied in more recent developments in a number of internet of things (IoT) domains. A number of research only examine how current methods are used to predict cardiovascular disease. In this work, we introduce a narrative approach to identify important possibilities through the application of machine learning methods that improve disorder prediction accuracy. Numerous options and classification strategies are used in the construction of the prediction model. We typically deliver an improved performance level with an accuracy level through the hybrid random forest with a linear model used in the prediction model for cardiovascular sickness.

Machine Learning, Supervised Learning, Heart disease, prediction, Imbalance data, etc.

"Heart Disease Prediction based on Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.e713-e719, December-2024, Available :http://www.jetir.org/papers/JETIR2412480.pdf

"Heart Disease Prediction based on Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppe713-e719, December-2024, Available at : http://www.jetir.org/papers/JETIR2412480.pdf