Paced resonance breathing in your terminal
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.
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%.
iOS application for the ENGAGE-HF heart failure study, recording Bluetooth vitals and generating medication recommendations.
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.
Building an open-source platform to foster international collaboration in the field of mechanical circulatory support
Firebase backend for the ENGAGE-HF heart failure study, providing cloud functions, data storage, and medication recommendations.
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
Web dashboard for the ENGAGE-HF heart failure study, giving clinicians access to participant vitals and medication recommendations.
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.
An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction
Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated17.9 million lives each year, which accounts for 31. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure. Most cardiovascular diseases can be prevented by addressing b…
In this project, we use a dataset external to Azure ML ecosystem to train and deploy models using AutoML and HyperDrive services.
Voice interface for the ENGAGE-HF heart failure study, connecting participants to a conversational assistant by telephone.
MENTORSHIP - Study of 12 clinical features por predicting death events
This repository consists of resources for learning EDA(Exploratory Data Analysis)
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