Early identification and intervention often leads to improved life outcomes for individuals with Autism Spectrum Disorder (ASD). However, traditional diagnostic methods are time-consuming, frequently delaying treatment. This study examines the application of machine learning (ML) techniques to 10-question Quantitative Checklist for Autism in Toddlers (QCHAT-10) datasets, aiming to evaluate the predictive value of questionnaire features and overall accuracy metrics across different cultures. We trained models using three distinct datasets from three different countries: Poland, New Zealand, and Saudi Arabia. The New Zealand and Saudi Arabian-trained models were both tested on the Polish dataset, which consisted of diagnostic class labels derived from clinical diagnostic processes. The Decision Tree, Random Forest, and XGBoost models were evaluated, with XGBoost consistently performing best. Feature importance rankings revealed little consistency across models; however, Recursive Feature Elimination (RFE) to select the models with the four most predictive features retained three common features. Both models performed similarly on the Polish test dataset with clinical diagnostic labels, with the New Zealand models with all 13 features achieving an AUROC of 0.94 +/- 0.06, and the Saudi Model having an AUROC of 93% +/- 6. This compared favorably to the cross-validation analysis of a Polish-trained model, which had an AUROC of 94% +/- 5, suggesting that answers to the QCHAT-10 can be predictive of an official autism diagnosis, even across cultures. The New Zealand model with four features had an AUROC of 85% +/- 13, and the Saudi model had a similar result of 87% +/- 11. These results were somewhat lower than the Polish cross-validation AUROC of 91% +/- 5. Adjusting probability thresholds improved sensitivity in some models, which is crucial for screening tools. However, this threshold adjustment often resulted in low levels of specificity during the final testing phase. Our findings suggest that these screening tools may generalize well across cultures; however, more research is needed regarding differences in feature importance for different populations.

The authors have declared no competing interest.

This project is funded by the NIH Director's New Innovator Award (DP2) from the National Institutes of Health (award DP2-EB035858).

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The datasets used and analyzed during the current study are available from the following sources: 1. New Zealand QCHAT-10 Dataset: The autism screening data for toddlers collected by Dr. Fadi Fayez Thabtah is available from the ASDTests screening application repository. The dataset can be accessed at ASDTests Repository. 2. Polish QCHAT-10 Dataset: The dataset featuring QCHAT scores from Polish toddlers is publicly accessible and can be found at Mendeley data, data.mendeley.com/datasets/tmpkt2mfkg/2. 3. Saudi Arabia QCHAT-10 Dataset: This dataset was obtained from Kaggle and is publicly accessible. It can be downloaded from kaggle.com/datasets/asdpredictioninsaudi/asd-screening-data-for-toddlers-in-saudi-arabia.

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The datasets used and analyzed during the current study are available from the following sources: 1. New Zealand QCHAT-10 Dataset: The autism screening data for toddlers collected by Dr. Fadi Fayez Thabtah is available from the ASDTests screening application repository. The dataset can be accessed at ASDTests Repository. 2. Polish QCHAT-10 Dataset: The dataset featuring QCHAT scores from Polish toddlers is publicly accessible and can be found at Mendeley data, data.mendeley.com/datasets/tmpkt2mfkg/2. 3. Saudi Arabia QCHAT-10 Dataset: This dataset was obtained from Kaggle and is publicly accessible. It can be downloaded from kaggle.com/datasets/asdpredictioninsaudi/asd-screening-data-for-toddlers-in-saudi-arabia.