• SB.SNEHA NARAYAHNI
• L.MAHALAKSHMI
• Mrs.B. Arunmozhikalanchiyam

Heart disease of the most significant causes of morality in the world today . Prediction of cardio vascular disease is a critical challenge in the area of clinical data analysis.Machine learning(ML)has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the health care industry. In order to focus the research on real-world datasets rather than merely theoretical approaches and simulations, it would be highly desired to extend this work further. The suggested hybrid HRFLM approach combines the benefits of the Linear Method (LM) and Random Forest (RF) techniques. When it came to predicting heart disease, HRFLM turned out to be fairly accurate. The connection between mental health and heart health is significant and multifaceted.Strategies such as therapy, stress management techniques, regular physical activity, and social support can all play a role in improving mental health and reducing the risk of heart disease.

Heart disease prediction , Machine learning models , Random Forest , Deep Learning , Data preprocessing

"CARDIO DYSFUNCTION PREDICTOR USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.k618-k625, April-2024, Available :http://www.jetir.org/papers/JETIR2404A83.pdf

"CARDIO DYSFUNCTION PREDICTOR USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppk618-k625, April-2024, Available at : http://www.jetir.org/papers/JETIR2404A83.pdf