Machine learning based Heart Disease Detection

•Developed and deployed a scalable Heart Health Monitoring web application using Flask for backend development and HTML/CSS for responsive frontend design. •Implemented hybrid machine learning model using a Stacking ensemble technique, achieving an accuracy of over 90% in predicting the likelihood of heart disease based on a public health dataset of over 1000 records. •Optimized model performance by integrating multiple algorithms, resulting in a 15% improvement in prediction accuracy. Integrated a user-friendly dashboard to visualize health metrics and trends, improving patient insights into their heart health status.