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Health·The Trace·Dual reading·Published 2026-09-09

machine learning-based autism spectrum disorder prediction using data augmentation and feature selection techniques

Source article: Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques

Abstract: 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…

TRV-2026-1032Peer-reviewedPermanent record — cite & verify
Trace impact reading

Contested: both sides are scored from claims and sources, not community votes.

P 72The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 71The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques

"Mental Patients Against Stigma" by Mindy Fisher is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.

The quick read

This peer-reviewed review examined 26 studies from 2021 to 2024 on machine learning and deep learning for autism spectrum disorder prediction, focusing on data augmentation and feature selection methods. It categorized augmentation into conventional transformations and GAN-based synthetic generation, and feature selection into filter, wrapper, and embedded approaches, evaluating study quality with the Prediction Model Risk of Bias Assessment Tool.

The findings matter because early and accurate ASD diagnosis is hindered by subjective assessments and limited data, and while augmentation and feature selection may improve robustness and performance, the review found limited external validation and insufficient ablation analyses that reduce confidence in generalizability and clinical applicability, leaving uncertainty about true benefit without more transparent and robust validation.

Main points
  • Structured search across IEEE Xplore, PubMed, Scopus, and Google Scholar identified 26 peer-reviewed studies from 2021 to 2024.
  • Augmentation categorized into conventional geometric and color-space transformations and advanced GAN-based synthetic data generation.
  • Feature selection classified into filter, wrapper, and embedded methods including information gain, chi-square tests, recursive feature elimination, and elastic net regularization.
  • Methodological quality and risk of bias assessed using the Prediction Model Risk of Bias Assessment Tool.
Gain

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.

Problem

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.

The rundown

The review searched IEEE Xplore, PubMed, Scopus, and Google Scholar for 2021-2024 studies and included 26 peer-reviewed papers that explicitly applied data augmentation or feature selection to ASD prediction, assessing bias with the Prediction Model Risk of Bias Assessment Tool.

Augmentation was divided into conventional geometric and color-space transformations and advanced generative adversarial network-based synthetic data generation, while feature selection included information gain, chi-square tests, recursive feature elimination, and elastic net regularization, with authors noting frequent lack of ablation analyses and biological interpretation.

What this doesn’t fix

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.

Sources

Reader signal

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