Heart Disease Prediction Using Machine Learning

Francesco Soliani Master Thesis, SUNY Downstate Medical Center, Brooklyn (New York) https://www.linkedin.com/in/francesco-soliani-63ba22233/

Solving physionet2017 with RCRNN

Cardiofieds is a Collection of fields based on Standard Definitions

Human sinoatrial node computational anatomy repository.

Analyze cardiograms with complex networks toolset to find predict disease

Python package for preprocessing OpenSlide image files and their corresponding annotations for use with Machine Learning segmentation models.

Project web page "Statistical Model Building Strategies for Cardiologic Applications - New results and future challenges"

object oriented / redux library to draw on canvas with the echocardiology bull-eye example

Predicting First-Year Survival after Percutaneous Coronary Interventions: A Machine Learning-Based ShinyApp Web Application in R

This is a cardio tracker app for tracking heart rate, systolic pressure & diastolic pressure

Code for paper "Deciphering simultaneous heart conditions with spectrogram and explainable-AI approach".

Département de cardiologie

A Tck/Tk GUI to plot continuous blood pressure waveforms

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

[CHIL 2024] Interpretation of Intracardiac Electrograms Through Textual Representations

R code for the data managment and statistical analysis performed for Association with and outcomes after non-cardiology vs. cardiology care in heart failure: Observations from SwedeHF

Cardiology hub written in Java 13, Thymeleaf and Bootstrap

CardioSTAT is an advanced R-based framework for heart disease classification and prediction, integrating statistical and machine learning approaches. This hybrid model combines ordinal regression with XGBoost for accurate classification and severity prediction, while offering extensive ROC analysis, statistical testing, and visualization tools.

An advanced ECG anomaly detection system using deep learning. This repository contains a CNN autoencoder trained on the PTBDB dataset to identify abnormal heart rhythms. It employs various loss functions for model optimization and provides comprehensive visualizations of the results.