Across the Spectrum: A Study of Autism in National Survey Data Using Machine Learning - He, David (Transactions on Computer Science and Intelligent Systems Research, 2024)
Autism, a neurological disorder and developmental impairment, affects roughly 1 in 36 children in the US. However, relatively few machine learning algorithms, the majority being Logistic Regression models, have been used to predict autism using national data. In this study, other than the Logistic Regression algorithm, four other Machine Learning (ML) algorithms, namely the Random Forest, KNN, Extreme Gradient Boosting, and Support Vector Classifier algorithms, are applied to the NSCH data collected in 2021-2022 (namely, the National -Survey-‘(Data)’ of-Children’s-Health), with a 7:3 training to testing split. Additionally, three resampling methods—’random over sampling, ‘random under sampling’, and ‘SMOTE’—are leveraged to address class imbalance issues. Furthermore, the Shapley Additive ex-Planation (SHAP) values of specific features are visualized and evaluated for their importance. The scalability and potential of these ML algorithms in predicting ASD is explored. Among the children with autism, 77.5% were male, 45.1% had ADHD, 39.0% had allergies, 28.1% had a genetic condition, 29.8% had experienced parents’ divorce, and 22.0% had experienced household hardship. From the SHAP selection, sex, ADHD, genetic conditions, low birth weight, allergies, certain ACEs, and other family factors are identified as important features. The precisions across five ML models are consistent around 95% while the recalls vary from 77% to 92% and F1 Scores range from 84% to 93%. The accuracies are between 0.77 and 0.92. This study demonstrates potential ML models applications in further understanding autism features of children, towards providing early assessment and tailoring data-driven evidence-based interventions.