{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T07:44:30Z","timestamp":1792395870134,"version":"4.4.0"},"reference-count":58,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":22,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To identify clinical profiles associated with conduct disorder (CD) among adolescents with attention\u2010deficit\/hyperactivity disorder (ADHD) using integrated statistical and machine learning approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A total of 138 adolescents aged 11\u201318 years (100 ADHD; 38 ADHD+CD) were assessed using multidimensional sociodemographic and psychometric measures. Ninety\u2010nine variables were analyzed. Feature selection and classification analyses were performed using \u03c7\n                      <jats:sup>2<\/jats:sup>\n                      and mRMR methods combined with supervised machine learning models, including logistic regression, support vector machine (SVM), decision tree, random forest, gradient boosting, and artificial neural networks. Model performance was evaluated using accuracy, recall, specificity, and the area under the receiver operating characteristic curve (AUC).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Low maternal education, disciplinary punishment, school absenteeism, medication\u2010free periods, peer smoking and delinquency, family criminal history, physical abuse, and later ADHD diagnosis (\u226512 years) consistently emerged as key factors associated with CD. In the exploratory comparison, the Top\u201020 SVM had the highest observed accuracy and F1\u2010score among the evaluated configurations (accuracy = 0.87; AUC = 0.90; F1\u2010score = 0.71), with high specificity and moderate sensitivity; no single model\u2010selection metric was prespecified.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.71784","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T05:35:44Z","timestamp":1790228144000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning\u2013Based Classification of Conduct Disorder Comorbidity in Youth With ADHD"],"prefix":"10.1002","volume":"16","author":[{"given":"B\u00fc\u015fra","family":"\u00c7a\u011f\u0131r","sequence":"first","affiliation":[{"name":"Department of Child and Adolescent Psychiatry Erciyes University  Kayseri Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rifat","family":"Kurban","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering Abdullah G\u00fcl University  Kayseri Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sevgi","family":"\u00d6zmen","sequence":"additional","affiliation":[{"name":"Department of Child and Adolescent Psychiatry Erciyes University  Kayseri Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0165\u20100327(98)00113\u2010X"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.books.9780890425596"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/00004583\u2010199709000\u201000013"},{"key":"e_1_2_11_5_1","first-page":"76","article-title":"Follow\u2010Up and Course, Prognostic Factors, and the Impact of Early Treatment in Oppositional Defiant Disorder and Conduct Disorder","volume":"5","author":"Arman A. 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