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Kidney Disease Detection Using Machine Learning Web App With Flask My SQL Database


(1 customer review)

The Kidney Disease Detection Web App leverages Python Flask and MySQL to offer advanced machine learning predictions with Shapley-based model explanations, enhancing doctor trust. It supports both individual and bulk patient predictions, alongside a feature-rich admin panel for model training and selection.


This is a complete end-to-end Kidney Disease Detection Machine Learning Web App. Designed with a dual-purpose interface, it enables administrators to train multiple machine learning models, maintain a comprehensive model registry, and seamlessly select the most accurate model for prediction. This innovative application delivers precise disease predictions and enhances doctors’ trust and reliability in its results by providing model explanations through Shapley values. Furthermore, it caters to a wide range of user needs by offering the flexibility to predict kidney disease for individual patients or execute bulk predictions for multiple patients simultaneously, ensuring a versatile and user-friendly experience for medical professionals.

Once purchase project Download Link will be available instantly.

Project Includes:

  1. Demo  Video:
  2. Working Flask App source code
  3. Model building Notebook
  4. MySQL Schema file with dummy data to start with & MySQL installation guide
  5. Flask App setup guide for step by step installing the app on your machine
  6. Full Project Presentation
  7. Project Report
  8. Project Synopsis
  9. Project Architecture
  10. 5 Reference research papers
  11. Online support

Features Of This Project

Admin :

  1. User Management –  Manage Admin User(Doctors/Nurses).
  2. Model Training – Train Machine Learning models by uploading kidney dataset.
  3. Model History–  View the list of trained models and allows to select one of them for prediction.
  4. Model Prediction- Individual Prediction and Bulk Prediction.
  5. View Prediction- This will show all historical model predictions done by all system users.


  1. Model Prediction- Individual Prediction and Bulk Prediction.
  2. View Prediction- This will show all historical model predictions done by all system users.

Technology Used 

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. MySQL: MySQL  database has been used as database for the project
  7. Flask: Project has been developed using the Flask Framework
  8. Shapley: Model Explanation

Installation Steps:-

  1. Install Python >=3.7
  2. Download and Install MySQL Workbench
  3. Run sql_data_schema.sql file on MySQL Workbench
  4. Install all dependencies cmd –pip install -r requirements.txt
  5. Finally, run cmd – python
  6. Admin User Id-
  7. Admin Password – admin

Also you can follow Flaskapp_Setup_Guide to step wise install the app on your machine.

Important Note:

Please be advised that the Kidney Disease Detection and Management Web App described herein is developed as an academic project and is intended for demonstration and educational purposes only. It should not be used for clinical diagnosis or treatment without undergoing thorough testing and validation in accordance with established medical standards and guidelines. This application is a representation of potential technological advancements in healthcare but has not been certified by medical associations for clinical use. Users are strongly discouraged from relying on this application for clinical purposes until it has been rigorously tested and approved by relevant regulatory bodies and medical professionals. We urge all users to exercise caution and consult with qualified healthcare providers for any medical diagnosis or treatment options.

1 review for Kidney Disease Detection Using Machine Learning Web App With Flask My SQL Database

  1. Somnath Suresh Shinde (verified owner)

    Basically the service which are provided by the team is excellent. It can helps to run the program and the team programmers are next level master in coding.

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