Early diagnosis of autism across developmental stages through scalable and interpretable ensemble model.

Journal: Frontiers in artificial intelligence
Published Date: May 30, 2025

Abstract

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition that challenges early diagnosis due to its diverse manifestations across different developmental stages. Timely and accurate detection is essential to enable interventions that significantly enhance developmental outcomes. This study introduces a robust and interpretable machine learning framework to diagnose ASD using questionnaire data. The proposed framework leverages a stacked ensemble model, combining Random Forest (RF), Extra Tree (ET), and CatBoost (CB) as base classifiers, with an Artificial Neural Network (ANN) serving as the meta-classifier. The methodology addresses class imbalance using Safe-Level SMOTE, dimensionality reduction via Principal Component Analysis (PCA), and feature selection using Mutual Information and Pearson correlation. Evaluation on publicly available datasets representing toddlers, children, adolescents, adults, and a merged dataset (Combining children, adolescents, and adults dataset) demonstrates high diagnostic accuracy, achieving 99.86%, 99.68%, 98.17%, 99.89%, and 96.96%, respectively. Comparative analysis with standard machine learning models underscores the superior performance of the proposed framework. SHapley Additive exPlanations (SHAP) were used to interpret feature importance, while Monte Carlo Dropout (MCD) quantified uncertainty in predictions. This framework provides a scalable, interpretable, and reliable solution for ASD screening across diverse populations and developmental stages.

Authors

  • Nasirul Mumenin
    Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
  • Maisha Mumtaz Rahman
    Department of Electrical and Electronics Engineering, Islamic University of Technology, Dhaka, Bangladesh.
  • Mohammad Abu Yousuf
    Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh. Electronic address: yousuf@juniv.edu.
  • Farzan M Noori
    Department of Informatics, University of Oslo, Oslo, Norway.
  • Md Zia Uddin
    Sustainable Communication Technologies, SINTEF Digital, Oslo, Norway.

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