Disease Data Organizer

AI-powered data organizer that classifies, structures, and visualizes medical datasets using configurable AI APIs.

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What It Does

Takes raw medical datasets (diabetes, brain stroke, heart disease) and uses AI-powered classification APIs to automatically categorize, label, and structure data for analysis. Compares 4 classification strategies to find the best organizer per dataset.

Key Features:

• AI classification via configurable API key
• 4 model comparison — Logistic Regression, Random Forest, SVM, Neural Network
• Interactive dashboard — accuracy, precision, recall, F1-score visualization
• REST API — batch data organization and classification

Architecture

Raw CSV Data → Preprocessing → AI Classification Engine (API Key)
                                  ├── Logistic Regression
                                  ├── Random Forest
                                  ├── SVM
                                  └── Neural Network
                                → Compare → Best Model → Organized Output
                                                          ├── React Dashboard
                                                          └── REST API

Tech Stack

Component	Technology
Classification	AI API + scikit-learn
Backend	Python, Flask
Frontend	React
Data	NumPy, Pandas, CSV datasets

My Role

I designed the data pipeline, selected classification algorithms, planned the API-key-based AI integration, and scoped the evaluation metrics. Code generation was accelerated using AI tools; feature engineering and model evaluation are mine.

Quick Start

git clone https://github.com/AdityaPandey-DEV/disease-classification-ml.git && cd disease-classification-ml
cd backend && pip install -r requirements.txt
export AI_API_KEY=your_key && python app.py    # → :5000
cd ../frontend && npm install && npm start      # → :3000

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Architected & built by Aditya Pandey — AI-augmented development