Flask based web app with five machine learning models on the 10 most common disease prediction, covid19 prediction, breast cancer, chronic kidney disease and heart disease predictions with their symptoms as inputs or medical report (pdf format) as input.

Final year project (Heart disease prediction using machine learning techniques)

A machine learning web application used to depict presence of heart disease, made using Random Forest Classifier and Flask. Deployed on pythonanywhere

A heart disease predictor application with additional features like contacting doctors, viewing report and report generation

Heart Disease Analysis

A system that predicts the risk of heart diseases in patients using information such as chest pain, cholesterol, thalassemia, exercise-induced angina, ECG, etc.

This is a flask application which attempt to create a model to predict heart disease using some basic information from the user.

E-Health Care is a web application where users can check their heart condition through this system. If the user's inserted symptoms match the symptoms of a disease, it will advise the associate physician for that disease.

A Random Forest Classifier to determine if the patient is likely to have Heart Disease based on given medical condition

This is a cardio tracker app for tracking heart rate, systolic pressure & diastolic pressure

A predictive analytics project aimed at identifying the likelihood of heart disease based on patient data using machine learning techniques. This repository includes data preprocessing, feature engineering, model training, and evaluation steps to deliver an effective and efficient predictive model.