Coronary artery stenosis detection using Faster RCNN

A Stacking-Based Model for Non-Invasive Detection of Coronary Heart Disease

VasculAR - Integration of Deep Learning into automatic volumetric cardiovascular dissection and reconstruction in simulated 3D space for medical practice

Code accompanying the paper 'Automatic Coronary Artery Plaque Quantification and CAD-RADS Prediction using Mesh Priors' (IEEE-TMI)

Coronary heart disease analysis, dataset - https://www.kaggle.com/datasets/billbasener/coronary-heart-disease. For analysis i used: k-means clustering, k-neighbors classifier, decision tree classifier. Libraries: scikit-learn.

Statistical Learning Project, Data Science @ UniPD. Prediction of Coronary Artery Disease using Statistical Learning Models

The project goal is to predict whether the patient has a 10-year risk of future coronary heart disease (CHD). The dataset is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts.

Identified the drivers of the risk of coronary heart disease and cardiovascular disease using the Sleep Heart Health Study dataset

This is a web application that aims to help with the diagnosis of various diseases such as breast cancer, chronic kidney disease, coronary heart disease, liver disease, and diabetes mellitus by analyzing different parameters. The application employs machine learning models on the backend, which is a technique of integrating several ML models

Coronary artery disease prediction based on polygenical risk scores and biochemical factors

Exploring diverse machine learning models to identify an optimal predictor for accurately forecasting the 10-year risk of diagnosing Coronary Artery Disease. Leveraging a range of health indicators and predictors, this project aims to enhance prediction accuracy and contribute valuable insights into proactive healthcare.