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<p>Univariate neuroimaging studies have shown brain differences in individuals with autism spectrum disorder (ASD) compared to healthy controls (CTL). In contrast, together with neuroimaging, machine learning (ML) provides a framework for building ASD diagnostic models with predictive accuracy assessed with cross-validation. Three types of ML methods in nine ML algorithms were investigated, i.e., <i>boosting</i>, <i>bagging</i>, and <i>neural networks</i>, to check the best algorithm for the classification of ASD from CTL using structural magnetic resonance imaging (MRI) data (<i>N</i> = 740, 344 ASD) from the Autism Brain Imaging Data Exchange (ABIDE) repository. The current study investigated model efficiencies in receiver operating characteristics (ROCs) during the training phase; and balanced accuracy used in the testing phase was captured to compare the algorithms. Findings showed Stochastic Gradient Boosting Machine (SGBM) with a balanced accuracy of 78.87% was the best algorithm for classifying ASD from CTL, followed by random forest (RF) and averaged neural networks (Av_NNET). Top brain features include right Heschl gyrus, left median cingulate and paracingulate gyri, left inferior occipital gyrus, right supramarginal gyrus, and left posterior cingulate gyrus. Findings predict that the ensemble algorithms of ML with multi-modal brain features will improve the accuracy of diagnostic models.</p>

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Classification and Feature Selection of Autism Spectrum Disorder Using MRI Data: A Machine Learning Approach

  • 期刊论文

  • 发表日期：

    2025年3月13日

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  • Gulshan Chauhan,
  • A. Jiran Meitei,
  • Budhachandra Khundrakpam,
  • Kh.Jitenkumar Singh,
  • Nongzaimayum Tawfeeq Alee

摘要

Univariate neuroimaging studies have shown brain differences in individuals with autism spectrum disorder (ASD) compared to healthy controls (CTL). In contrast, together with neuroimaging, machine learning (ML) provides a framework for building ASD diagnostic models with predictive accuracy assessed with cross-validation. Three types of ML methods in nine ML algorithms were investigated, i.e., boosting, bagging, and neural networks, to check the best algorithm for the classification of ASD from CTL using structural magnetic resonance imaging (MRI) data (N = 740, 344 ASD) from the Autism Brain Imaging Data Exchange (ABIDE) repository. The current study investigated model efficiencies in receiver operating characteristics (ROCs) during the training phase; and balanced accuracy used in the testing phase was captured to compare the algorithms. Findings showed Stochastic Gradient Boosting Machine (SGBM) with a balanced accuracy of 78.87% was the best algorithm for classifying ASD from CTL, followed by random forest (RF) and averaged neural networks (Av_NNET). Top brain features include right Heschl gyrus, left median cingulate and paracingulate gyri, left inferior occipital gyrus, right supramarginal gyrus, and left posterior cingulate gyrus. Findings predict that the ensemble algorithms of ML with multi-modal brain features will improve the accuracy of diagnostic models.