The RNN for Cardiovascular Disease Detection project is an innovative application of deep learning techniques to detect and predict cardiovascular diseases using recurrent neural networks (RNNs). Built using Python, TensorFlow, and Keras, this project aims to provide a reliable tool for early detection and diagnosis of cardiovascular diseases.

Cystatin C and Cardiovascular Disease: A Mendelian Randomization Study.

A package for running experiments with VAEs for ECG data, including a supervised head designed for survival analysis of cardiovascular disease events.

Analysis of patient data from a kaggle dataset to assess if tall people's risk of developing cardiovascular disease was higher than short people's.

An R script doing a basic statistical analysis and modeling of CVD/excess death trends in the U.S.

Ensemble Learning

Cardio-vascular-disease-prediction

Code for paper "Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance", Bonnemain et al., 2021, Frontiers in Cardiovascular Medicine

An end to end ML model to predict whether a person has cardiovascular disease or not based on various features.

This project comprises predicting different types of disease at one place Pneumonia, Malaria, Liver Disease and Cardiovascular Disease

🫀 Code for "Neural network-based integration of polygenic and clinical information: Development and validation of a prediction model for 10 year risk of major adverse cardiac events in the UK Biobank cohort" 🫀