Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

Public repository associated with: Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs

⛑ Code for "Metabolomic profiles predict individual multi-disease outcomes" ⛑

[Project Repo] Predicting cardiovascular diseases.

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).

Public repository associated with: "Multi-Label ECG Classification Using Convolutional Neural Networks in a Classifier Chain"

Code for MICCAI 2023 publication: SCOL: Supervised Contrastive Ordinal Loss for Abdominal Aortic Calcification Scoring on Vertebral Fracture Assessment Scans

Awesome Heart Sound Analysis - A Survey

This Project is based upon a CHDs (Cardiovascular Heart Diseases) research dataset which has over 3000 records and 16 attributes. Since, the target variable belongs to Categorical attribute, We built classification models for the future predictions of CHDs in patients considering the features.

Supervised ML - Classification Using Python this project demonstrates the effectiveness of machine learning techniques in predicting cardiovascular risk using the Framingham Heart Study dataset. The developed machine learning model can be used by healthcare professionals to identify individuals at high risk of cardiovascular disease .

A dataset containing over 70,000 data points, 12 features, and one target variable were used to analyze if machine learning could predict if an individual has cardiovascular disease.

ECG classification using public data and state-of-the-art 1D CNN models. This work is based on George Moody Challenge 2020

Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank

Developpement of a machine learning model (SVM classifier) for cardiovascular disease prediction. Deployed on a streamlit app.

EpiCardio is a shiny app built using R, which allows you to visualise trends in cardiovascular disease mortality in Cuba between 2010 and 2020.

It is a Capstone project. A model has been created to predict for the heart diseases. It can be very useful for the health sector as cardiovascular diseases are rapidly increasing. The record contains patients' information. It includes over 4,000 records and 15 attributes.

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated17.9 million lives each year, which accounts for 31. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure. Most cardiovascular diseases can be prevented by addressing b…

An implementation of the Framingham CVD risk score with DMN

A SAS analysis project on cardiovascular disease data