Enhanced Cardiovascular Risk Prediction Using ML Powered Web Application

This paper discusses a machine learning (ML) application tailored for predicting heart disease risk, examining different models that are efficient in risk assessment. Our study encompasses the comprehensive implementation of various Machine learning algorithms as well as their comparison, including decision trees, random forests, logistic regression, support vector machines, and neural networks. The chosen ML model is then deployed through a web application, prioritizing a user-friendly platform towards ensuring public accessibility. Our results underscore the remarkable efficacy of ML in precisely forecasting specific risk of heart diseases and thus its web deployment enhances its potential and opens it up to a larger audience. Moreover, our findings advocate for the seamless integration of advanced machine learning methods within health risk assessment frameworks. In essence, this research highlights that, by leveraging sophisticated algorithms and prioritizing accessibility, we aim to propel the adoption of ML-powered solutions in healthcare, ultimately fostering proactive health management and preventive care initiatives.