WITHDRAWN: Bridging the Gap: Explainable AI for Autism Diagnosis and Parental Support with TabPFNMix and SHAP

doi:10.21203/rs.3.rs-6250369/v1

WITHDRAWN: Bridging the Gap: Explainable AI for Autism Diagnosis and Parental Support with TabPFNMix and SHAP

2025 · doi:10.21203/rs.3.rs-6250369/v1

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Abstract

Abstract Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that affects a growing number of individuals worldwide. Despite extensive research, the underlying causes of ASD remain largely unknown, with genetic predisposition, parental history, and environmental influences identified as potential risk factors. Diagnosing ASD remains challenging due to its highly variable presentation and overlap with other neurodevelopmental disorders. Early and accurate diagnosis is crucial for timely intervention, which can significantly improve developmental outcomes and parental support. This work presents a novel artificial intelligence (AI) and explainable AI (XAI)-based framework to enhance ASD diagnosis and provide interpretable insights for medical professionals and caregivers. The proposed framework leverages advanced classification models, specifically the TabPFNMix regressor, which is optimized for structured medical datasets. Unlike traditional machine learning methods, TabPFNMix demonstrates superior performance in capturing complex ASD-related patterns. To address the black-box nature of AI models, Shapley Additive Explanations (SHAP) is integrated to provide transparent and interpretable reasoning behind the model’s decisions, ensuring better understanding for clinicians and caregivers. Extensive experiments were conducted using a publicly available benchmark dataset, with performance evaluated through standard metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Comparative analysis with baseline models, including Random Forest, XGBoost, Support Vector Machine (SVM), and Deep Neural Networks (DNNs), demonstrates that TabPFNMix achieves the highest accuracy (91.5%), surpassing XGBoost (87.3%) by 4.2 percentage points. Additionally, it attains superior recall (92.7%), precision (90.2%), F1-score (91.4%), and AUC-ROC (94.3%), ensuring both high diagnostic accuracy and robustness in real-world ASD screening. An ablation study highlights the significance of feature selection and preprocessing, revealing that omitting key features or preprocessing steps (such as normalization and missing data imputation) significantly degrades performance. Furthermore, SHAP-based feature importance analysis identifies social responsiveness scores, repetitive behavior scales, and parental age at birth as the most influential factors in ASD diagnosis. These insights align with medical literature, reinforcing the reliability of the model’s predictions and its applicability in clinical settings.

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The published version of this preprint was retracted from *Scientific Reports* in December 2025. The retraction notice can be found here . Research Square has withdrawn this preprint. Editorial notes are used to provide important context regarding the topic of a preprint or to alert readers to potential issues concerning that preprint or a downstream publication associated with it. For more information on editorial notes, see our Editorial Policies . Abstract 8 December 2025. The published version of this preprint was retracted from *Scientific Reports* in December 2025. The retraction notice can be found here . Research Square has withdrawn this preprint. Full Text 8 December 2025. The published version of this preprint was retracted from Scientific Reports in December 2025. The retraction notice can be found here . Research Square has withdrawn this preprint. Cite Share Download PDF Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Scientific Reports → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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