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

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

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.

Detect kidney stones from X-Ray images

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.

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

Predicted chronic kidney disease using biomarkers with Random Forest

Chronic kidney disease (CKD) is a long-term disorder which causes the kidneys to not function as well as they should.Our goal is to predict whether a subject has the chance of getting chronic kidney disease from a given set of data using machine learning.

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

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

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 …

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

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.

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

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 …

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 …