README.md

metadata

title: Autism Screening AI
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.41.0
app_file: streamlit_app.py
pinned: false

🧠 AI-Powered Autism Screening System

Early detection of autism spectrum disorder (ASD) using machine learning and explainable AI.

📁 Project Structure

autism/
├── data/                      # Dataset & data fetching scripts
│   ├── autism_screening.csv   # Main dataset (704 records)
│   └── fetch_dataset.py       # Download script
├── notebooks/                 # Jupyter notebooks
│   ├── 01_eda_and_data_loading.ipynb
│   ├── 02_model_training.ipynb
│   └── 03_explainability.ipynb
├── models/                    # Saved ML models
├── results/                   # Analysis outputs & visualizations
└── README.md

🚀 Quick Start

1. Get the Dataset

Option A: Download Automatically

cd data
python fetch_dataset.py

Option B: Download Manually

• Download from Kaggle (704 records)
• Or UCI ML Repository
• Save as data/autism_screening.csv

Option C: Start with Sample Data

• A sample dataset will be created automatically if real data isn't found

2. Run the Analysis Notebook

# Make sure you're in the project root
jupyter notebook notebooks/01_eda_and_data_loading.ipynb

📊 What's Included

Notebook 1: EDA & Data Loading

• ✅ Load 704-record autism screening dataset
• ✅ Analyze class balance (autism vs. non-autism)
• ✅ Check for missing values & data completeness
• ✅ Statistical feature analysis
• ✅ Quality assessment report

Notebook 2: Model Training (Coming)

• Build baseline model (Logistic Regression)
• Compare models (Random Forest, SVM, etc.)
• Cross-validation & performance metrics
• Train-test split strategy

Notebook 3: Explainability (Coming)

• SHAP values for feature importance
• Interpretable results for non-technical users
• Risk factor identification
• Confidence scoring

🎯 Dataset Info

Size: 704 adult screening records
Target: Binary classification (Autism: Yes/No)
Features: ~20-30 features based on screening questionnaires (AQ-10, etc.)
Class Distribution: Typically ~30% positive, ~70% negative

📋 Questionnaire Features

Common screening features include:

• Social attention & awareness
• Communication patterns
• Focused attention
• Imagination abilities
• Pattern recognition
• Memory for details
• Social relationships
• Anxiety levels
• Voice tone understanding

⚙️ Requirements

pandas
numpy
matplotlib
seaborn
scikit-learn
jupyter
shap (for explainability)

Install all at once:

pip install pandas numpy matplotlib seaborn scikit-learn jupyter shap

📈 Next Steps

1. Load the data → Run Notebook 01
2. Explore patterns → Check class balance & features
3. Build models → Run Notebook 02
4. Explain results → Run Notebook 03
5. Deploy UI → Build Streamlit app (optional)

🔒 Disclaimer

⚠️ This tool is for screening support only, not medical diagnosis.

• Always consult with healthcare professionals
• Intended for educational & awareness purposes
• Not a substitute for professional evaluation

📚 Resources

• Autism Spectrum Australia
• DSM-5 Diagnostic Criteria
• UCI ML Autism Dataset

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Ready to explore? Start with Notebook 01! 🚀