ArticleBrain and behavior2026
Machine Learning-Based Classification of Conduct Disorder Comorbidity in Youth With ADHD.
Article in Brain and behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo identify clinical profiles associated with conduct disorder (CD) among adolescents with attention-deficit/hyperactivity disorder (ADHD) using integrated statistical and machine learning approaches.
methodsA total of 138 adolescents aged 11-18 years (100 ADHD; 38 ADHD+CD) were assessed using multidimensional sociodemographic and psychometric measures. Ninety-nine variables were analyzed. Feature selection and classification analyses were performed using χ
resultsLow maternal education, disciplinary punishment, school absenteeism, medication-free periods, peer smoking and delinquency, family criminal history, physical abuse, and later ADHD diagnosis (≥12 years) consistently emerged as key factors associated with CD. In the exploratory comparison, the Top-20 SVM had the highest observed accuracy and F1-score among the evaluated configurations (accuracy = 0.87; AUC = 0.90; F1-score = 0.71), with high specificity and moderate sensitivity; no single model-selection metric was prespecified.
conclusionThese findings demonstrate that machine learning can identify clinical profiles associated with CD comorbidity in adolescents with ADHD. Such approaches may complement comprehensive clinical assessment but should not be interpreted as tools for predicting future CD development.
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