Archicava/autism-detector
112
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
Output
{
"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
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)
- Healthy controls: Include children with ADHD, speech delays, behavioral issues (non-ASD)
- 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
Confusion Matrix (Test Set, n=83)
Grid Search
Best hyperparameters found via 3-fold cross-validation:
- Hidden layers: [64, 32]
- Dropout: 0.2
- Learning rate: 0.001
- Epochs: 300
Limitations
- Trained on limited dataset (415 samples)
- Healthy controls include children with ADHD, speech delays, and behavioral issues (not ASD)
- Not validated across diverse populations
- Screening tool only, not diagnostic
- Requires all 8 input fields
- May have reduced sensitivity for atypical ASD presentations
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 (4 in test set) may delay intervention
- Model trained to distinguish ASD from ADHD/speech delays - not a replacement for differential diagnosis
Files
Citation
@misc{asd_detector_2026,
title={Autism Spectrum Disorder Screening Model},
year={2026},
publisher={Archicava},
url={https://huggingface.co/archicava/autism-detector}
}