aceMachine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent difficulties in social communication, social interaction, and repetitive behaviors. Early and accurate diagnosis is essential but is often hindered by subjective clinical assessments, limited data availability, and inconsistencies in existing diagnostic tools. This review evaluates the role of machine learning and deep learning approaches in improving ASD prediction, with a particular focus on two important yet r…
Data augmentation and feature selection techniques may improve robustness, predictive performance, and interpretability of machine learning models for autism spectrum disorder prediction and help address dataset scarcity.
Machine learning models for autism spectrum disorder prediction that use data augmentation and feature selection have limited external validation and inadequate evaluation frameworks, reducing confidence in reported performance improvements and model generalizability.
Review identifies limited external validation, insufficient ablation analyses, and inadequate evaluation frameworks that reduce confidence in reported improvements and generalizability, with augmentation and feature selection often applied without empirical justification or biological interpretation.
Evidence
- Peer-reviewedHealth Care Science2026-09-07
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Truvace Impact Record TRV-2026-1032, v1: “Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques.” Truvace, 2026-09-09. /record/TRV-2026-1032 (accessed at citation time). sha256 21261d9e9c93ff31…
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