Analisys of the dataset Heart Failures clinical records from UCI using different rebalancing techiniques and different models

World Health Organization has estimated 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the United States and other developed countries are due to cardio vascular diseases.

Rule-based healthcare expert system designed using Pyke and Python. The project focuses on heart failure telemonitoring, aiming to enhance patient self-care and clinical management.

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%.

Building an open-source platform to foster international collaboration in the field of mechanical circulatory support

Metadata files for the idr0042 submission

Code and Datasets for the paper "DG-Viz: Deep Visual Analytics with Domain Knowledge Guided Recurrent Neural Networks on Electronic Health Records", published on Journal of Medical Internet Research (JMIR) in 2020.

Your own 🤖 Doctor

MENTORSHIP - Study of 12 clinical features por predicting death events

This is a Machine Learning and Deep Learning project that can predict the chances of getting diseases like Heart_Failure, Diabetes, Malaria and Tuberculosis.

Utilizing Principal Component Analysis (PCA) for insightful feature reduction and predictive modeling, this GitHub repository offers a comprehensive approach to forecasting heart disease risks. Explore detailed data analysis, PCA implementation, and machine learning algorithms to predict and understand factors contributing to heart health.

It is a Capstone project. A model has been created to predict for the heart diseases. It can be very useful for the health sector as cardiovascular diseases are rapidly increasing. The record contains patients' information. It includes over 4,000 records and 15 attributes.

An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

12 clinical features for predicting death events.

In this project, we use a dataset external to Azure ML ecosystem to train and deploy models using AutoML and HyperDrive services.

This repository consists of resources for learning EDA(Exploratory Data Analysis)

R code for the data managment and statistical analyses for Eligibility for sacubitril/valsartan in SwedeHF.

R code for the data managment and statistical analysis performed for Association between B-Blockers and Outcomes in HFpEF - Current Insights from the SwedeHF Registry.

It's a straightforward Matlab code that can predict the patient's heart failure.

Introduction to PERMIT project resources