A machine learning web application use to predict chances of heart disease, built with FLASK and deployed on Heroku.
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Updated
Apr 26, 2024 - HTML
A machine learning web application use to predict chances of heart disease, built with FLASK and deployed on Heroku.
Heart disease risk prediction with LightGBM
A heart disease detection project using machine learning to analyze patient data and predict the likelihood of heart disease. The project aims to aid healthcare professionals in early detection, enabling timely interventions and improving patient outcomes.
A 10-year Coronary Heart disease predictor developed for people among various age groups from an appropriate dataset based on several parameters using HTML, CSS and Django and used Logistic Regression Model for prediction.
Heart Diseases Prediction using scikit-learn
A system that predicts the risk of heart diseases in patients using information such as chest pain, cholesterol, thalassemia, exercise-induced angina, ECG, etc.
Deploying a ML model using flask on web
Heart Attack Risk Predictor uses data collected from Apple Health and user's input to predict risk of a heart attack in women. Project idea by Selina Vicino and Tatiana Patton for ASU BMI. Faculty mentor: Dr. Dongwen Wang. Beta version can be installed by following the link below.
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