2026· ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)· Vol 12, pp. 400-407· 0 citations
TL;DR
A toddler‑centered Case‑Based Reasoning (CBR) framework that emulates clinicians’ decision‑making by retrieving and adapting similar historical cases and delivers transparent, interpretable recommendations via comparable cases, supporting clinician trust is introduced.
Abstract
Early diagnosis of Autism Spectrum Disorder (ASD) in toddlers improves developmental outcomes through timely interventions. However, traditional methods rely on subjective judgment, lengthy assessments, and limited specialist availability. This study introduces a toddler‑centered Case‑Based Reasoning (CBR) framework that emulates clinicians’ decision‑making by retrieving and adapting similar historical cases. The system processes screening data from children aged 12–36 months, including demographics, risk factors, and three core behavioral domains: social interaction, communication, and repetitive behaviors. Cases are organized hierarchically by age and severity. The framework implements the full CBR cycle: structured representation, similarity‑based retrieval via clinically weighted Euclidean distance, DSM‑5–inspired rule‑guided adaptation, and dynamic maintenance through validation and pruning. On a public toddler ASD screening dataset, it achieves 84.2% accuracy, 79.3% sensitivity, 89.7% specificity, and an F1‑score of 0.82 using similarity‑weighted voting over top‑k cases, outperforming Random Forest (81.5% accuracy) and k‑NN (78.9% accuracy) baselines trained on the same features. It also delivers transparent, interpretable recommendations via comparable cases, supporting clinician trust. This clinically aligned CBR system provides an accessible AI tool for early toddler ASD diagnosis in healthcare settings.
Behavioral observation through examiner-child structured interactive play, particularly metrics related to eye contact, effectively differentiates toddlers with iASD.
Dan Ai, Binyue Hu, Qiuhong Wei et al.· BMC Psychiatry· 0 citations
Machine learning can support diagnosticians in this effort, as demonstrated here utilizing multiple rating scales, the TASI, and the TAP, but there is a risk for bias when using machine learning and as such, no algorithm should replace expert clinical judgment.
Aaron J. Kaat, Ashlynn Campagna, Hannah Feiner et al.· JCPP Advances· 0 citations
A Hybrid Intelligent Model designed to predict ASD in pediatric cases, leveraging adaptive neuro-fuzzy systems integrates artificial neural network capabilities with fuzzy logic, offering a comprehensive approach to ASD prediction.
N. Okafor, C. Ituma, R. Nweze· Communication in Physical Sc...· 0 citations
Selecting Evidence-Based Practices learning strategies for children with Autism Spectrum Disorder requires careful consideration of children’s diverse ability profiles. This study developed a classification model for Evidence-Based Practices learning strategies using the Random Forest algorithm based on a primary dataset consisting of 106 records. The features used include child age, gender, verbal ability, Autism Spectrum Disorder severity level, learning media, and language learning difficulties. The classification target consists of four classes: ABA, Visual Method, PECS, and Speech Therapy. Model evaluation was conducted using an 80:20 hold-out split, Stratified 5-Fold Cross Validation, Repeated Stratified Cross Validation, ablation test, and feature importance analysis. In the 80:20 hold-out scenario, Random Forest achieved an accuracy of 45.45% and an F1-Macro score of 0.4393. In Stratified 5-Fold Cross Validation, the model obtained an average accuracy of 38.61% ± 7.17% and an F1-Macro score of 0.3803 ± 0.0736. The repeated cross-validation results showed an average accuracy of 34.45% ± 9.46% and an F1-Macro score of 0.3332 ± 0.0967. These findings indicate that Random Forest is able to form an initial classification model; however, its performance remains low to moderate. The limitations of this study lie in the limited dataset size, the relatively small number of samples per class, and the absence of external validation; therefore, the model cannot yet be used as a final recommendation system. This study is positioned as a preliminary study to support the development of a Decision Support System for selecting Evidence-Based Practices learning strategies for children with Autism Spectrum Disorder.
S. Septiana, Dwi Krisbiantoro, Suliswaningsih Suliswaningsih· Jambura Journal of Electrica...· 0 citations
The Mandarin DREAM-IT provides clinically useful information about early language and communication development in Mandarin-speaking toddlers with ASD and DLD and shows moderate-to-strong concurrent alignment with established developmental and autism-related measures.
Jinzhu Zhao, Dandan Wu, Yutong Li et al.· International journal of lan...· 0 citations
It is suggested that eye-tracking-based ML models may provide a promising approach for early screening of developmental conditions and ASD-GDD differentiation and future studies should validate these findings in larger, multicenter, and more balanced samples with richer dynamic eye-tracking representations.
Gang Zhou, Xiaobin Zhang, Xingda Qu et al.· Research in Developmental Di...· 0 citations
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