---
library_name: pytorch
license: mit
tags:
  - tabular
  - structured-data
  - binary-classification
  - medical
  - autism
  - screening
language:
  - en
metrics:
  - accuracy
  - f1
  - roc_auc
---

# Autism Spectrum Disorder Screening Model

## Model Description

A feedforward neural network for autism spectrum disorder (ASD) risk screening using 8 structured clinical input features.

**Important:** This is a screening tool, NOT a diagnostic instrument. Results must be interpreted by qualified healthcare professionals.

## Intended Use

- **Primary use:** Clinical decision support for ASD screening
- **Users:** Healthcare professionals, clinical software systems
- **Out of scope:** Self-diagnosis, definitive diagnosis

## Input Features

| Field | Type | Valid Values | Description |
|-------|------|--------------|-------------|
| `developmental_milestones` | categorical | `N`, `G`, `M`, `C` | Normal, Global delay, Motor delay, Cognitive delay |
| `iq_dq` | numeric | 20-150 | IQ or Developmental Quotient |
| `intellectual_disability` | categorical | `N`, `F70.0`, `F71`, `F72` | None, Mild, Moderate, Severe (ICD-10) |
| `language_disorder` | binary | `N`, `Y` | No / Yes |
| `language_development` | categorical | `N`, `delay`, `A` | Normal, Delayed, Absent |
| `dysmorphism` | binary | `NO`, `Y` | No / Yes |
| `behaviour_disorder` | binary | `N`, `Y` | No / Yes |
| `neurological_exam` | text | non-empty string | `N` for normal, or description |

## Output

```json
{
  "prediction": "Healthy" | "ASD",
  "probability": 0.0-1.0,
  "risk_level": "low" | "medium" | "high"
}
```

### Risk Level Thresholds
- **Low:** probability < 0.4
- **Medium:** 0.4 ≤ probability < 0.7
- **High:** probability ≥ 0.7

## How to Use

```python
import json
import torch
from pathlib import Path
from huggingface_hub import snapshot_download

# Download model
model_dir = Path(snapshot_download("toderian/autism-detector"))

# Load config
with open(model_dir / "preprocessor_config.json") as f:
    preprocess_config = json.load(f)

# Load model
model = torch.jit.load(model_dir / "autism_detector_traced.pt")
model.eval()

# Preprocessing function
def preprocess(data, config):
    features = []
    for feature_name in config["feature_order"]:
        if feature_name in config["categorical_features"]:
            feat_config = config["categorical_features"][feature_name]
            if feat_config["type"] == "text_binary":
                value = 0 if data[feature_name].upper() == feat_config["normal_value"] else 1
            else:
                value = feat_config["mapping"][data[feature_name]]
        else:
            feat_config = config["numeric_features"][feature_name]
            raw = float(data[feature_name])
            value = (raw - feat_config["min"]) / (feat_config["max"] - feat_config["min"])
        features.append(value)
    return torch.tensor([features], dtype=torch.float32)

# Example inference
input_data = {
    "developmental_milestones": "N",
    "iq_dq": 85,
    "intellectual_disability": "N",
    "language_disorder": "N",
    "language_development": "N",
    "dysmorphism": "NO",
    "behaviour_disorder": "N",
    "neurological_exam": "N"
}

input_tensor = preprocess(input_data, preprocess_config)
with torch.no_grad():
    output = model(input_tensor)
    probs = torch.softmax(output, dim=-1)
    asd_probability = probs[0, 1].item()

print(f"ASD Probability: {asd_probability:.2%}")
print(f"Prediction: {'ASD' if asd_probability > 0.5 else 'Healthy'}")
```

## Training Details

- **Dataset:** 315 ASD patients + 100 healthy controls (415 total)
- **Preprocessing:** Min-max normalization for numeric, label encoding for categorical
- **Architecture:** Feedforward NN (input → 64 → 32 → 2)
- **Loss:** Cross-entropy
- **Optimizer:** Adam (lr=0.001)

## Evaluation

| Metric | Value |
|--------|-------|
| Accuracy | 0.9759 |
| F1 Score | 0.9839 |
| ROC-AUC | 0.9913 |
| Sensitivity | 0.9683 |
| Specificity | 1.0000 |

### Confusion Matrix (Test Set, n=83)

|  | Predicted Healthy | Predicted ASD |
|--|-------------------|---------------|
| Actual Healthy | 20 | 0 |
| Actual ASD | 2 | 61 |

## Limitations

- Trained on limited dataset (415 samples)
- Healthy controls are synthetically generated
- Not validated across diverse populations
- Screening tool only, not diagnostic
- Requires all 8 input fields

## Ethical Considerations

- Results should always be reviewed by qualified professionals
- Should not be used as sole basis for clinical decisions
- Model performance may vary across different populations
- False negatives (2 in test set) may delay intervention

## Files

| File | Description |
|------|-------------|
| `autism_detector_traced.pt` | TorchScript model (load with `torch.jit.load()`) |
| `config.json` | Model architecture configuration |
| `preprocessor_config.json` | Feature preprocessing rules (JSON, no pickle) |
| `model.py` | Model class definition |
| `requirements.txt` | Python dependencies |

## Citation

```bibtex
@misc{asd_detector_2024,
  title={Autism Spectrum Disorder Screening Model},
  year={2024},
  publisher={HuggingFace},
  url={https://huggingface.co/toderian/autism-detector}
}
```