Heart disease prediction and Kidney disease prediction. The whole code is built on different Machine learning techniques and built on website using Django

Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.

A rule-based algorithm enabled the automatic extraction of disease labels from tens of thousands of radiology reports. These weak labels were used to create deep learning models to classify multiple diseases for three different organ systems in body CT.

Digitization, Analysis & Prediction of Medical Reports using Deep Learning.

Through this project, ONC in partnership with National Institutes of Health (NIH) National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), advanced the application of AI/ML in patient-centered outcomes research (PCOR) by generating high quality training datasets for a chronic kidney disease (CKD) use case – predicting mortality …

HealthOrzo is a Disease Prediction and Information Website. It is user friendly and very dynamic in it's prediction. The Project Predicts 4 diseases that are Diabetes , Kidney Disease , Heart Ailment and Liver Disease . All these 4 Machine Learning Models are integrated in a website using Flask at the backend .

chronic kidney disease detection using different neural network technique

A Python library for kidney failure risk estimation using Tangri's KFRE model

SKINET Project is meant to perform a segmentation of a kidney's biopsy or a nephrectomy and recognize the different histological structures.

Through this project, ONC in partnership with National Institutes of Health (NIH) National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), advanced the application of AI/ML in patient-centered outcomes research (PCOR) by generating high quality training datasets for a chronic kidney disease (CKD) use case – predicting mortality …

chronic kidney disease is a machine learning project used to predict the chronic disease in a patient

This repository presents a comprehensive classification project that leverages relevant features to accurately predict Patient with Kidney Stone.

Detect kidney stones from X-Ray images

SKINET Project is meant to perform a segmentation of a kidney's biopsy or a nephrectomy and recognize the different histological structures. By doing that, it is possible to analyze kidneys more precisely and get a better understanding of their behaviors. This is an updated version of the original Skinet Tool that provides indicators to compute …

Demonstrating how changes in input image resolution affect the algorithm's output

The Kidney Stone Prediction Classifier is a binary classification model developed to predict whether a patient is likely to have kidney stones based on various numerical features.

Contribution to the #EHH2022 Challenge #6 "Are you kidneying". Prototype XGBoost Model by Roman Dusek to identify early lab markers of Kidney Disease. Additional Support by Francis Chemorion and Benjamin Senst.

This repository holds all the project files belongs to a Kidney diesease classification application which takes x-rays images and classify the image as dieseased or healthy by using Deep learning CNN classification techniques.

A machine learning application, deployed using Flask, is designed to identify the presence of kidney disease in patients by analyzing various medical features.