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  • Deep Learning
  • AI

Explainable TabNet Medical Diagnosis and Hospital Recommendation System

An explainable AI platform that predicts disease and treatment cost, then recommends the best-fit hospital.

Overview

TabNet predicts a likely disease from symptoms or an uploaded medical document and estimates approximate treatment cost through a regression head. AHP and TOPSIS then rank nearby hospitals on cost, distance and facilities using Google Maps and Geopy, while SHAP, LIME and counterfactual analysis explain every prediction the system makes.

Problem Statement

A patient with symptoms faces two separate unknowns — what is likely wrong, and where they can afford to get it treated. Diagnosis tools ignore cost entirely, and hospital search ignores the diagnosis.

Proposed Solution

Predict the condition and its likely treatment cost together, then use multi-criteria decision analysis to rank hospitals against that specific need — with every step explained rather than asserted.

Key Features

  • TabNet-Based Disease Prediction

    Predicts disease from symptoms or uploaded medical documents.

  • Treatment Cost Estimation

    TabNet regression model estimates approximate treatment cost.

  • Hospital Recommendation

    AHP & TOPSIS-based ranking using cost, distance, and facilities via Google Maps/Geopy.

  • Explainable AI

    SHAP, LIME, and counterfactual analysis explain every prediction.

Technologies Used

Deep Learning

  • TabNet
  • PyTorch
  • Tabular modelling

Decision analysis

  • AHP
  • TOPSIS
  • Geopy
  • Google Maps API

Explainability

  • SHAP
  • LIME
  • Counterfactual analysis

Application

  • Flask / Streamlit
  • Pandas
  • PostgreSQL

Project Workflow

  1. 1

    Symptom intake

    Symptoms or a medical document are submitted.

  2. 2

    Disease prediction

    TabNet predicts the likely condition.

  3. 3

    Cost estimation

    A regression head estimates treatment cost.

  4. 4

    Hospital ranking

    AHP and TOPSIS rank hospitals on cost, distance and facilities.

  5. 5

    Explanation

    SHAP, LIME and counterfactuals justify each output.

What You'll Receive

  • Complete, runnable source code with folder structure
  • Project report and technical documentation
  • Ready-to-present PPT content
  • Setup and installation walkthrough
  • Line-by-line project explanation session
  • Viva question bank with answers
  • Bug fixing and troubleshooting help
  • Post-delivery support after submission

Frequently Asked Questions

How much does this project cost?

Every project is quoted individually, because the price depends on the modules you need, your technology stack, your college's format and your deadline. Send us the project name on WhatsApp and we'll share a quote the same day.

Can the project be customised to my college requirements?

Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.

Will I be able to explain this project during my viva?

That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.

What if the project does not run on my laptop?

We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.

Do I get support after submission?

Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.

Want This Project?

Get complete source code, documentation, PPT, explanation, and support.

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