Flask based web app with five machine learning models on the 10 most common disease prediction, covid19 prediction, breast cancer, chronic kidney disease and heart disease predictions with their symptoms as inputs or medical report (pdf format) as input.

Classification of Kidney disease using Sklearn and Custom ML Algorithms

I made a model which can detect if a person has Chronic Kidney Disease by inputting some data. I also made a WebApp using Heroku

Test-run of our reproducibility protocol on Allen et al. 2020: "A simulation modelling toolkit for organising outpatient dialysis services during the COVID-19 pandemic"

Enhancing Chronic Kidney Disease Prediction through Data Science methdologies

Chronic Risk Prediction Web App 🌐🔍. Harnessing Random Forest, SVC, and Decision Tree classifiers, it offers predictions on chronic health risks. Seamlessly integrated as a microservice, it fetches input from an image using OCR which it receives from external server.

Chronic kidney disease prediction done using Artificial Neural Network.

Includes the full data report with SQL queries, pdf presentation, and original dataset file

In the present research work, we aim to build efficient tools for predicting CKD occurrence, following an approach that exploits ML techniques.

Predicting chronic kidney disease stages using patient data.

Ensemble Learning

This research demonstrates the potential of machine learning for non-invasive CKD detection and emphasizes the importance of addressing current limitations to develop scalable and cost-effective screening tools for early CKD intervention, improving public health outcomes globally.

This project is focused on using the Chronic Kidney Disease (CKD) dataset, which is obtained from the UCI Machine Learning Repository. The dataset contains medical attributes related to kidney disease, and the goal is to build a MLP(Artificial Neural Network)model to predict whether a patient has chronic kidney disease (CKD) or not.

Chronic Kidney Disease Prediction Using ML | Website |

The Chronic Kidney Disease Detection project uses machine learning and deep learning models to predict chronic kidney disease from demographic and medical data. The models, including XGBoost, CatBoost, and an ANN-based deep learning approach, achieve an accuracy of 99%. Explainable AI (XAI) techniques like SHAP and LIME are employed.

NC-Scorer is a tool designed to standardize and automate the assessment of candidate gene variants identified through genetic sequencing for Chronic Kidney Disease (CKDu) research. Utilizing the Nephro Candidate Score (NCS), it accelerates the discovery of genetic associations in CKD.