Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

AI application that can predict the survival of patients with heart failure using 12 clinical features.

An interactive Power BI dashboard analyzing a heart disease dataset to uncover key patterns and risk factors. Using Power Query for data transformation and Power BI for visualization, this project explores how features like sex, age, chest pain type, and ST segment slope correlate with heart disease outcomes.

Analysis and SQL associated with Clinical Practice Groups across Royal Free London NHS FT

Predicting Mortality among a Cohort of patients with Heart Failure

ReHeaT: The Reference for the Heart failure Transcriptome

We have to predict a person death event using some features:- - Age ,Gender , blood pressure, smoke, diabetes,ejection fraction, creatinine phosphokinase, serum_creatinine, serum_sodium, time

Heart Failure dataset analysis using homemade neural networks and SKlearns algorithms

Leveraging machine learning techniques to predict the likelihood of heart failure in patients based on a comprehensive dataset of patient information sourced from Kaggle.

Predicting mortality risk in heart failure patients using machine learning models on clinical data by Python

Benchmarking Machine Learning Framework for Heart Failure Outcome Prediction Using Clinical Data

This is a public repo to accompany the paper submission and meta-analysis of untargeted metabolomics data from heart failure patients.

This repo is for the heart failure analysis using Machine Learning approaches.

paper readings