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  • Deep Learning
  • Blockchain
  • Gen-AI

HemoGuard: Blockchain-Enabled, Gen-AI–Powered Hematologic Toxicity Predictor

A predictive healthcare system for detecting hematologic toxicity following chemotherapy treatment.

Overview

Deep learning models predict a patient's risk of anemia, neutropenia, thrombocytopenia and combined toxicity after chemotherapy. Predictions are stored immutably on-chain, and a generative AI layer produces clinician-readable explanations to support treatment decisions.

Problem Statement

Hematologic toxicity after chemotherapy is often caught only when a patient's counts have already crashed. Risk indicators are present in prior lab work, but reviewing them manually for every patient before every cycle is impractical.

Proposed Solution

Predict toxicity risk per class before the next cycle, explain the prediction in clinical language, and record it immutably so treatment decisions have a defensible, tamper-evident trail.

Key Features

  • Hematologic Toxicity Detection

    Identifies anemia, neutropenia, thrombocytopenia, and combined toxicity risk.

  • Blockchain-Based Storage

    Secure, immutable storage of patient prediction records.

  • Gen-AI Clinical Insights

    AI-generated explanations to support treatment decisions.

Technologies Used

Deep Learning

  • TensorFlow / Keras
  • Multi-label classification
  • NumPy

Gen-AI

  • LLM clinical summarisation
  • Prompt templates

Blockchain

  • Solidity
  • Smart contracts
  • SHA-256 hashing

Application

  • Flask
  • React
  • PostgreSQL

Project Workflow

  1. 1

    Patient data intake

    Lab values and treatment history are collected.

  2. 2

    Risk prediction

    The model scores each hematologic toxicity class.

  3. 3

    Clinical explanation

    Generative AI turns the prediction into readable reasoning.

  4. 4

    Immutable record

    The prediction is hashed and committed on-chain.

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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