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.

🏥 AI-powered system for Chronic Kidney Disease staging and treatment. Combines a stacking ensemble (CatBoost, XGBoost, LightGBM) with clinical rules to achieve 96.8% accuracy. Includes SHAP explainability, a Streamlit web app, PDF reports, and expert-aligned treatment guidance. Built for clinicians, researchers, and ML developers in healthcare AI.

Classification of Kidney disease using Sklearn and Custom ML Algorithms

This project is a full-stack machine learning web app designed to predict Chronic Kidney Disease (CKD) from patient data. Built with Flask, scikit-learn, and a clean front-end, it offers real-time predictions through a user-friendly interface.

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

[eClinicalMedicine] Identification and validation of an explainable early-stage chronic kidney disease prediction model: a multicenter retrospective study

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.

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"

Chronic kidney disease prediction done using Artificial Neural Network.

Enhancing Chronic Kidney Disease Prediction through Data Science methdologies

The transition probabilities for 25 transitions from initial state to each intermediate and absorbing states were estimated using a Kaplan-Meier test through the wide-format datset. Transition probabilities for hazard functions were estimated using the lifelines package in Python within a Visual Studio Code integrated development environment.

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.

Chronic Kidney Disease Prediction Using ML | Website |

Ensemble Learning

Early Prediction for Chronic Kidney Disease Detection: A Progressive Approach to Health Management

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.

kidney.epi R package by Scientific-Tools.Org