• Dr. S. Haseena
• E Priyadharshini
• V Shamili

Globally, one of the main causes of death is heart disease. A significant challenge in clinical data analysis is predicting cardiovascular disease. The ability of AI to decide and anticipate a vast amount of information generated by the health sector has been established. By utilizing machine learning techniques, we offer a special technique for identifying crucial qualities. In order to acquire the correct results, doctors must attempt to save human lives. As a result, a system was created to forecast the likelihood of developing heart disease as well as common symptoms such age, gender, glucose, smoke, and cholesterol. Doctors utilize these to verify the health of their patients. In order to increase accuracy, autocorrelation, auto regression have been utilized along with machine learning techniques like multiple linear regression and correlation coefficient. This work increases the accuracy of diagnosing cardiac problems. The classification method used to develop the suggested system include support vector machine (SVM). By using this classification model, a single best-fit predictive model is produced. Numerous investigations have been made in order to identify heart infection, however the accuracy of the results

cardiovascular disease, regression, correlation, prediction

" Analysis and Prediction of Cardiovascular Disease using Machine Learning Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.51-55, June-2023, Available :http://www.jetir.org/papers/JETIRFZ06009.pdf

" Analysis and Prediction of Cardiovascular Disease using Machine Learning Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. pp51-55, June-2023, Available at : http://www.jetir.org/papers/JETIRFZ06009.pdf