What question did this study set out to answer?

The aim is to investigate machine learning algorithms' ability to distinguish adolescents with Major Depressive Disorder from healthy controls based on neurocognitive data.

February 5, 2026Clinical Child Psychology and Psychiatry0 citations

Neurocognitive and Social Cognitive Predictors of Adolescent Major Depressive Disorder: A Machine Learning Classification Study

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YSY. Saglam STSeyma Takir ÇEÇağatay Ermiş

Key Points

The aim is to investigate machine learning algorithms' ability to distinguish adolescents with Major Depressive Disorder from healthy controls based on neurocognitive data.
Used structured interviews to assess adolescents with MDD and healthy controls.
Measured neurocognitive functions through various cognitive tests.
Performed feature selection using a tree-based approach and multiple machine learning algorithms.
Addressed class imbalance with Synthetic Minority Over-sampling Technique and optimized model performance using stratified 10-fold cross-validation.
Computed SHAP values to interpret the contributions of different features.
The Support Vector Classifier achieved the highest mean accuracy of 76.0% and a mean Area Under Curve of 79.0%.
Ridge Classifier and Linear Discriminant Analysis both achieved an accuracy of 71.8%.
Bagging Classifier achieved 71.2%, while Random Forest and Gaussian Naive Bayes both achieved 69.6%.
SHAP values identified symbol coding, categorical fluency, and Stroop Test parameters as crucial features influencing classification.

Abstract

Background/aim This study aimed to investigate whether machine learning (ML) algorithms could accurately differentiate adolescents with Major Depressive Disorder (MDD) from healthy controls (HC) based on neurocognitive data. Method Adolescents diagnosed with MDD and HC were assessed using structured interviews, and neurocognitive functions were measured via tests for verbal and visual memory, working memory, executive functions, processing speed, inhibition, verbal fluency, and social cognition/Theory of Mind skills. Feature selection was performed using a tree-based approach and implemented through multiple ML algorithms. To address class imbalance, ML models were trained with Synthetic Minority Over-sampling Technique, and model performance was optimized using stratified 10-fold cross-validation (CV). Shapley Additive Explanations (SHAP) values were computed to interpret feature contributions. Results A total of 117 MDD and 67 HC adolescents were included in the study. The Support Vector Classifier (SVC) achieved the highest performance, with a mean accuracy = 76.0% (range min–max = 71.1%–80.9%), and a mean Area Under Curve (AUC) = 79.0%, (range min–max = 74.7%–82.4%); followed by Ridge Classifier (accuracy = 71.8% 65.6%–78.0%), Linear Discriminant Analysis (accuracy = 71.8% 67.2%–76.4%), Bagging Classifier (accuracy = 71.2% 63.7%–78.7%), Random Forest (accuracy = 69.6% 61.8%–77.4%), Gaussian Naive Bayes (accuracy = 69.6% (63.5%–75.7%]), Ridge Classifier CV (accuracy = 69.1% 62.5%–75.7%) and Multilayer Perceptron (accuracy = 65.3% 57.5%–73.2%). SHAP value identified symbol coding, categorical fluency and Stroop Test parameters as the most influential features. Conclusions ML techniques showed good performance in distinguishing adolescents with MDD from HC, with SVC achieving the highest accuracy. Cognitive domains related to processing speed and executive functions appear to be clinically relevant, suggesting that future studies should explore their role in first-episode, medication-naive adolescents and assess whether ML-based cognitive profiling can support early recognition.

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Cite This Study

Saglam et al. (Mon,) studied this question.

synapsesocial.com/papers/6984349af1d9ada3c1fb2f2c https://doi.org/https://doi.org/10.1177/13591045261421320

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