It is a medical chatbot that will provide quick answers to FAQs by setting up rule-based keyword chatbots.

Heart disease prediction and Kidney disease prediction. The whole code is built on different Machine learning techniques and built on website using Django

Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.

This project will focus on predicting heart disease using neural networks. Based on attributes such as blood pressure, cholestoral levels, heart rate, and other characteristic attributes, patients will be classified according to varying degrees of coronary artery disease

A machine learning web application use to predict chances of heart disease, built with FLASK and deployed on Heroku.

Heart disease prediction system Project using Machine Learning with Code and Report

Heart disease prediction using normal models and hybrid random forest linear model (HRFLM)

Final Year Project Heart Disease Prediction Project with all Documents.

Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection (Physionet Challenge 2022)

Predict the risk factors for heart disease.

A soft computing method based web project which helps in predicting the disease based on the symptoms of the patient. Also informs the patients about nearby doctors availability and precautions to be taken. The heart of the project is Fuzzy Logic , a soft computing technique which makes use of knowledge base made by the experts(doctors in this c…

Deployed medical apps on streamlit

❤️ Cardio Guide is an application which uses Machine Learning Model to predict the chances of Heart Disease with an accuracy of 81.967%. With this it also provide you with tips to improve your health status which directly benefits your heart.

This project is combination of data analysis and machine learning. In this project First I try to find features and make different three type of models. Do some analysis and finally getting accuracy of 86% on test data

Heart disease prediction with logistic regression using SAS Studio. The dataset is taken from UCI Machine Learning about heart disease.

This dataset is contain different parameter information of heart disease patient, based on given feature we need to predict the patient has heart disease or not

Multiple Disease Prediction System

Data Analyisis of heart disease.

Cardio Monitor is a web app that helps you to find out whether you are at risk of developing heart disease. the model used for prediction has an accuracy of 92%. This is the course project of subject Big Data Analytics (BCSE0158).