Optimizing 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.

RL4CAD: Personalized Decision Making for Coronary Artery Disease Treatment using Offline Reinforcement Learning

Improve understanding of x-ray coronary angiography images in different quiz modules

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

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.

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

Coronary artery stenosis detection using Faster RCNN

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

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

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

Utilizing a suite of machine learning algorithms, this project accurately predicts coronary heart disease by analyzing patient data, with Random Forest outperforming as the most effective classifier.

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.

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