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. 2022 Jun 9;93(2):300–307. doi: 10.1038/s41390-022-02137-1

Table 2.

Examples of machine learning methods applied in neurodevelopmental paediatrics.

Authors Das and Khanna75 Sen et al.76 Emerson et al.79 Girault et al.80 Kim et al.81
Year published 2019 2018 2017 2019 2021
Purpose Diagnostic Diagnostic Prognostic Prognostic Diagnostic
Outcome ADHD ADHD ASD at 24 months of age Cognitive ability at age 2 years Developmental disability
Outcome measure Diagnosed by child neurologist using DSM-IV criteria Diagnostic criteria differed across study sites DSM-IV criteria Mullens Scales of Early Ability. Outcome categorised as above or below the median Diagnosis made by paediatric psychologist
Features Pupillometric data Structural and functional MRI MRI at 6 months of age MRI at birth Drag and drop data from computer-based game
Participants 50 (28 cases, 22 controls) 558 (279 cases, 279 controls) 59 infants with high familial risk of ASD (11 cases, 48 controls) 115 infants 370 children (147 cases, 233 controls)
Algorithms trained LR, SVM, KNN, NB, DT, RF SVM SVM DL DL
Evaluation method Nested 10-fold cross-validation Independent test set (77 cases, 94 controls) Cross-validation with nested leave one out procedure Independent test set (22 cases, 15 controls) 10-fold cross-validation
Evaluation result (best performing models) NB: AUROC 0.871, sensitivity 0.656, specificity 0.863 Accuracy 0.6725, sensitivity 0.4545, specificity 0.8510 Accuracy 0.966, sensitivity 0.818, specificity 1.000 Accuracy 0.838, sensitivity 0.864, specificity 0.800 AUROC 0.817, sensitivity 0.757, specificity 0.740

LR logistic regression, SVM support vector machine, KNN K-nearest neighbours, NB naive Bayes, DT decision tree, RF random forest, ADHD attention deficit hyperactivity disorder, ASD autism spectrum disorder, DSM-IV diagnostic and statistics manual of mental disorders.