Predict heart failure risk with an XGBoost ML model on clinical data. Features are engineered, scaled, and deployed via a Flask web app for real-time predictions. This project aims to support early intervention and better patient outcomes.

No description available

No description available

No description available

Training two models, one with with AutoML & one with HyperDrive, compare, and deploy the best model as a service - A Machine Learning Engineer Project

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.

Cardiovascular diseases are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Early detection, and managment of cardiovascular diseases can be a great way to manage the fatality rate associated with cardiovascular diseases, and this is where a machine learning model comes in. For the purpose of predicting the risk of a heart failure in patients, I used the Support Vector Classifier to build a machine learning model, and deployed it using Flask and Heroku

[IEEE trans on Big Data] LLM informed AI-ECG model for Heart Failure Risk Prediction

With the data obtained we try classifying if the patient is having a heart failure situation

No description available

This project aims to predict the occurrence of heart failure and extract its most important associative factors through multiple classificational algorithms.

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%.

Personal project using kaggle available dataset

Predicting Heart Failure using Ensemble Learning with Spark

Mortality Prediction in Heart Failure Patients Using the MIMIC III ICU Medical Records Database

No description available

Artificial Intelligence Enabled Prediction of Heart Failure Risk from Single-lead Electrocardiograms

A Machine Learning-based Heart Failure Prediction System using Flask and AdaBoost.

A machine learning web app that can predict if a person will die of heart failure or not.

Introduction In my case studies I keep writing in English because it is used in Kaggle and I also keep them in Portuguese because my native language is Brazilian Portuguese, so we can share more knowledge and experiences in Kaggle with our Brazilian colleagues. We will develop and analyze the algorithms with the best capacity and identify the problems in the heart and at the end we will make a comparison between them. Description Context Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help. Attribute Information Age: age of the patient [years] Sex: sex of the patient [M: Male, F: Female] ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic] RestingBP: resting blood pressure [mm Hg] Cholesterol: serum cholesterol [mm/dl] FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise] RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria] MaxHR: maximum heart rate achieved [Numeric value between 60 and 202] ExerciseAngina: exercise-induced angina [Y: Yes, N: No] Oldpeak: oldpeak = ST [Numeric value measured in depression] ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping] HeartDisease: output class [1: heart disease, 0: Normal] Source This dataset was created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are: Cleveland: 303 observations Hungarian: 294 observations Switzerland: 123 observations Long Beach VA: 200 observations Stalog (Heart) Data Set: 270 observations Total: 1190 observations Duplicated: 272 observations Final dataset: 918 observations Every dataset used can be found under the Index of heart disease datasets from UCI Machine Learning Repository on the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/ Citation fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/heart-failure-prediction. Acknowledgements Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

Heart Disease Predictor app is used to classify whether the person has heart disease or not based on certain input parameters using python's scikit-learn, fastapi, numpy and joblib packages.

No description available

No description available

Prediction of Mortality Rate of Heart Failure Patients Admitted to ICU

Prediction of hospital stay duration in heart failure patients using machine learning.

This project contains the backend logic for heart rate failure prediction. The project is developed using Fast API.

Implementation of paper Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques https://ieeexplore.ieee.org/document/9370099

Heart Failure Prediction Using XGBoost Classification Model

Machine learning project to predict heart failure using clinical data

Heart Failure Prediction