🫀 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" 🫀

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

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

Cystatin C and Cardiovascular Disease: A Mendelian Randomization Study.

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

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

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.

Comparative Analysis for Various Classification Machine Learning Algorithms for Detecting Heart Diseases

This project contains a Python implementation of logistic regression to predict the risk of developing heart disease in the next 10 years, based on the Framingham dataset from Kaggle. The implementation achieved an accuracy of 87.27% on the test set. The code is available on GitHub under the repository name "HeartDiseaseRiskLR".

Ensemble Learning

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.

Cardio-vascular-disease-prediction