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

🏥 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.

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

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.

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.

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

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

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 |

A machine learning project focused on predicting chronic kidney disease (CKD) stages and performing survival analysis using clinical biomarkers. It utilizes the Kaplan-Meier estimator to analyze patient progression and visualize survival probabilities, offering insights into CKD management.

Ensemble Learning

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

This project focuses on predicting Chronic Kidney Disease (CKD) using neural networks. It involves comprehensive data preprocessing, exploratory data analysis, and model interpretability to provide a robust classification solution based on health records.

Predicting chronic kidney disease stages using patient data.