Heart Disease Prediction Using Machine Learning
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Updated
Apr 2, 2023 - Jupyter Notebook
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
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.
A Tck/Tk GUI to plot continuous blood pressure waveforms
Code for paper "Deciphering simultaneous heart conditions with spectrogram and explainable-AI approach".
This is a cardio tracker app for tracking heart rate, systolic pressure & diastolic pressure
Human sinoatrial node computational anatomy repository.
Cardiofieds is a Collection of fields based on Standard Definitions
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
Source code for "Slow Delayed Rectifier Protects Against Arrhythmic Activity Across Multiple Species - A Computational Study"
CTAMACE is a web application which can be used to predict major cardiovascular events (MACE) two years following coronary multidetector computed tomography (MDCT) using combined anatomical coronary findings and clinical features
Motion Analysis of Ring-Shaped tissues - code from Seguret et al. 2024 eLife
Cardioinformatics: the nexus of bioinformatics and precision cardiology
The ECG Detection with Deep Learning project employs Convolutional Neural Networks to automatically analyze Electrocardiogram (ECG) data, facilitating precise detection of cardiac abnormalities and enhancing diagnostic accuracy.
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
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