Web Based Leukemia Subtype Prediction Using Machine Learning

A web-based platform for predicting leukemia subtypes using machine learning. Built with Vite, React, TypeScript, Tailwind CSS, and Flask backend. Users can input gene expression features to get accurate ALL subtype predictions using models like Random Forest, KNN, and Logistic Regression.

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Web-Based-Leukemia-Subtype-Prediction-Using-Machine-Learning

A web-based leukemia subtype prediction tool using ML and DAA. Features include gene selection using mRMR and classification via KNN, SVM, and Random Forest. Built with Vite, React, TypeScript, Tailwind CSS (UI) and Flask (backend). Includes Knapsack for optimal feature or model selection

https://github.com/user-attachments/assets/ccd11527-c55f-46b2-b754-78d3f44362ca

🧬 LeucoPredic: Web-Based Leukemia Subtype Prediction Using Machine Learning

LeucoPredic is an AI-powered web platform that predicts subtypes of Acute Lymphoblastic Leukemia (ALL) based on gene expression input. Built with a modern tech stack, it integrates machine learning algorithms and discrete algorithmic approaches (DAA) like Knapsack and mRMR for optimal feature selection and classification accuracy.

🔍 Features

🧪 Predict ALL subtypes using gene expression data
🧠 Machine Learning models: KNN, SVM, Random Forest, Logistic Regression
🎯 Feature selection using mRMR (Minimum Redundancy Maximum Relevance)
📦 Discrete Algorithmic Approaches: Knapsack for optimal model/feature optimization
🌐 Modern, responsive UI built with Vite + React + TypeScript + Tailwind CSS + shadcn/ui
🔗 Flask backend for model integration and inference
📊 Displays prediction results with confidence scores and treatment guidance

🏥 Why This Matters

Accurate subtyping of leukemia is critical for determining the right treatment plan. This tool aids medical researchers and physicians by automating subtype prediction and reducing diagnostic errors through high-accuracy machine learning.

⚙️ Tech Stack

🧩 Frontend

Vite
React
TypeScript
Tailwind CSS
shadcn/ui

🔧 Backend

Python (Flask)
Scikit-learn (ML models)
Pandas, NumPy
Feature selection using skfeature or custom mRMR

🚀 Getting Started

Clone the repository

git clone https://github.com/your-username/leucopredic.git
cd leucopredic

Start the frontend

cd frontend
npm install
npm run dev

Run the backend

cd backend
pip install -r requirements.txt
python app.py

Navigate to localhost:5173 to use the app.

🧠 ML Models Used

Model	Purpose
K-Nearest Neighbors (KNN)	Fast, intuitive classification
Support Vector Machine (SVM)	Robust decision boundary
Random Forest	High-accuracy ensemble learning
Logistic Regression	Baseline interpretability model

📚 How it Works

User uploads gene expression data
Feature selection via mRMR or Knapsack algorithm
Prediction using trained ML models
Result visualization with subtype classification & confidence

📂 Data Sources

Public leukemia datasets from NCBI GEO
Gene expression profiles from microarray/RNA-seq studies

✨ Sample Output

Subtype: B-ALL with ETV6-RUNX1
Confidence: 92.7%
Suggested Therapy: Standard chemo with excellent prognosis

👨‍💻 Author

Harish S.S. Machine Learning & Bioinformatics Enthusiast Email: harishdeepikassdeepikass@gmail.com

📄 License

This project is licensed under the MIT License. See LICENSE file for details.

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