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Drug Recommendation & Medical condition classifier Flask web app in Python


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

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Heart Disease Detection Using Machine Learning Web App With Flask SQLite Database


The Heart Disease Detection Web App leverages Python Flask and SQLite3 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 Heart 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 heart disease for individual patients or execute bulk predictions for multiple patients simultaneously, ensuring a versatile and user-friendly experience for medical professionals.

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Project Includes:

  1. Demo  Video
  2. Working Flask App source code
  3. Flask App setup guide video
  4. Model building Notebook
  5. SQLite Database
  6. Full Project Presentation
  7. Project Report
  8. Project Synopsis
  9. 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 DB bowser for SQLite
  3. Install all dependencies cmd –python -m pip install -r requirements.txt
  4. Finally, run cmd – python
  5. Admin User Id-
  6. Admin Password – admin

Important Note:

Please be advised that the Heart 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.


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