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)
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

Metric	Value
Accuracy	0.9398
F1 Score	0.9600
ROC-AUC	0.9595
Sensitivity	0.9365
Specificity	0.9500

Confusion Matrix (Test Set, n=83)

	Predicted Healthy	Predicted ASD
Actual Healthy	19	1
Actual ASD	4	59

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

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_2026,
  title={Autism Spectrum Disorder Screening Model},
  year={2026},
  publisher={Archicava},
  url={https://huggingface.co/archicava/autism-detector}
}

Archicava/autism-detector

Autism Spectrum Disorder Screening Model

Model Description

Intended Use

Input Features

Output

Risk Level Thresholds

How to Use

Training Details

Evaluation

Confusion Matrix (Test Set, n=83)

Grid Search

Limitations

Ethical Considerations

Files

Citation

Archicava/
autism-detector