Comparative Analysis of Machine Learning Models for Heart Attack Prediction

Cardiovascular disease is considered as one of the major causes of passing within the day-to-day situation. It is related with certain propensities and wellbeing conditions like smoking, tall blood weight, and cholesterol. It too emphasizes on the rate of importance of chance factors. Predicting cardiac sickness may be a extreme work in restorative inquire about, and this work appears to an degree how to handle it. Exact forecasts are pivotal for moving forward healthcare strategies. This consider compares three unmistakable machine learning (ML) models: Logistic Regression, Decision Tree and CatBoost with the taking after precision rates: Logistic Regression(LR) (91.38%), Decision Tree (91%), and CatBoost (91.44%) the rates gotten here as the execution degree is promising. Interests, CatBoost turns out to be the best-performing model. These are utilized for the show and its potential to extend the exactness of heart assault expectations. As long as cardiovascular hazard components are common, the utilize of state-of-the-art calculations will be utilized for upgrading the preventative therapeutic intercessions.