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

A WebApp that predicts the likelihood of occurrence of Death Event due to Heart Failure. It into consideration twelve features that predict mortality by heart failure.

This is the implementation of "Congestive heart failure detection using random forest classifier" paper by Zerina Masetic and Abdulhamit Subasi.

Explore a modular, end-to-end solution for heart disease prediction in this repository. From problem definition to model evaluation, dive into detailed exploratory data analysis. Experience seamless integration with MLOps tools like DVC, MLflow, and Docker for enhanced workflow and reproducibility.

This project involves training of Machine Learning models to predict the Heart Failure for Heart Disease event. In this KNN gives a high Accuracy of 89%.

Application to predict 10 year risk of heart failure. The application also allows storage (consented) of submitted patient data + real-time analysis of the data in database. Machine learning model trained and tested using Python (FraminghamModel.ipynb) and deployed as a Django web app. see http://new-hf-predictor.herokuapp.com/ for demo

This repo contains machine learning projects for beginners.

Predicting readmission risk in heart failure patients using machine learning algorithms and patient data.

Binary Classification Project

Machine Learning based Heart Failure Detection

A heart failure prediction model, crafted through the utilization of pandas, numpy, seaborn, and matplotlib, holds immense potential for real-life impact. By analyzing key health indicators, such as age, blood pressure, and cholesterol levels, the model facilitates early identification of individuals at risk of heart failure.

predicting the risk of a heart failure

Using data about patients with a heart disease, I created a prediction model for the death event of a patient. I did extensive data preprocessing, added meaningful visualizations, and eventually created a Random Forest model for this problem. I used Pandas, Scikit-Learn, Seaborn, Matplotlib, Numpy, etc.

New decision support system in predicting heart failure using logistic regression algorithm

This repository contains a notebook that examines the performance of various classification models on the Kaggle dataset: https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data. The best performing model was a Random Forest Classifier with 86.67% accuracy.

Heart Failure Prediction Model (ML) Logistic Regression 85% Accuracy

Heart Failure Prediction with Model Deployment

A project about heart failure prediction using classification models. This project is related with MLZoomcamp 2022 midterm project.

This repository contains code and a dataset for predicting heart failure rates using PyTorch. The predictive model is built upon the "Heart Failure Clinical Records Dataset" obtained from Kaggle, which includes various clinical features related to heart health.

Developing, Evaluating, and Comparing different Classification Models on Heart Failure Prediction Dataset