There are 2 repositories under cardiovascular-diseases topic.
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.
Awesome Heart Sound Analysis - A Survey
Public repository associated with: "Multi-Label ECG Classification Using Convolutional Neural Networks in a Classifier Chain"
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).
Code for MICCAI 2023 publication: SCOL: Supervised Contrastive Ordinal Loss for Abdominal Aortic Calcification Scoring on Vertebral Fracture Assessment Scans
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.
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 .
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.
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.
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 behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyper lipidaemia or al-ready established disease) need early detection and management where in a machine learning model can be of great help
EpiCardio is a shiny app built using R, which allows you to visualise trends in cardiovascular disease mortality in Cuba between 2010 and 2020.
An implementation of the Framingham CVD risk score with DMN
Proyecto que busca predecir enfermedades cardiovasculares en pacientes potenciales, analizando una serie de factores de la salud cardiaca de los mismos, a partir de la ayuda de machine learning vía tres clasificadores de aprendizaje supervisado.
This repo contains a Machine Learning-based methodology for the preliminary design of a risk calculator using medical tabular databases, combining the knowledge of different clinically validated cardiovascular risk calculators using Transfer Learning (TL).
Blake's Haas Capstone Project - Patient Readmissions Prediction
Ensemble Learning
This project contains a Python implementation of logistic regression to predict the risk of developing heart disease in the next 10 years, based on the Framingham dataset from Kaggle. The implementation achieved an accuracy of 87.27% on the test set. The code is available on GitHub under the repository name "HeartDiseaseRiskLR".
INVESTIGATING THE ASSOCIATION BETWEEN POLYGENIC RISK SCORE OF ADIPOSE TISSUE FUNCTION AND CARDIOVASCULAR DISEASE
A Machine Learning model that predicts the occurrence of prevalent Stroke, Hypertension, Coronary Heart Disease and Diabetes using Framingham's dataset.
The project consists in building a Transformer Encoder to predict deaths from cardiovascular diseases. An important part is to exploit missing values in order not to lose data information. Data augmentation is performed by adding missing values and noise to training records.
Welcome to the Cardiovascular Disease Prediction Project! 💖❤️🔥 Cardiovascular disease (CVD) remains a global health challenge, accounting for significant morbidity and mortality rates worldwide. In response, I have developed an innovative deep learning model designed to predict CVD leveraging ANNs and CNNs.
Machine Learning based Cardiovascular Disease Detection
Projet d'analyse de données - Maladies cardiovasculaires- R
Automatic ECG classification using discrete wavelet transform and one-dimensional convolutional neural network
Detecting Heart disease in patients using svm
A Machine Learning project for Cardiovascular disease prediction
Cardiovascular Disease Prediction on 19 Lifestyle Factors