Autism spectrum disorder (ASD) is a neurodevelopmental disorder typically diagnosed in children. Early detection of ASD, particularly in girls who are often diagnosed late, can aid long-term development for children. We aimed to develop machine learning models for predicting ASD diagnosis in children, both boys and girls, using child-mother linked electronic health records (EHRs) data from a large clinical research network. Model features were children and mothers' risk factors in EHRs, including maternal health factors. We tested XGBoost and logistic regression with Random Oversampling (ROS) and Random Undersampling (RUS) to address imbalanced data. Logistic regression with RUS considering a three-year observation window for children's risk factors achieved the best performance for predicting ASD among the overall study population (AUROC = 0.798), boys (AUROC = 0.786), and girls (AUROC = 0.791). We calculated SHAP values to quantify the impacts of important clinical and sociodemographic risk factors.

The authors have declared no competing interest.

This study was supported by grant R21MH129682 from the National Institutes of Health (NIH). This study was also partially supported by NIH grants R01CA246418, R01CA246418-02S1, R21CA245858, R21CA245858-01A1S1, R21CA253394-01A1, R01AG080624, and R21AG068717. The authors wish to thank the Cancer Informatics Shared Resource in the UF Health Cancer Center for data analytics support.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Institutional Review Boards (IRB) of University of Florida waived ethical approval for this work. This is a secondary analysis of electronic health records, where a waiver of consent was approved with an approved protocol by the University of Florida IRB.

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All data produced in the present study are available upon reasonable request to the authors