IntroductionAttention Deficit Hyperactivity Disorder (ADHD) is a Neurodevelopmental Disorder characterized by persistent pattern of inattention and hyperactivity / impulsivity. There is considerable difficulty in diagnosing ADHD, mainly to discriminate what could be symptoms arising from ADHD or typical age behaviors. The decision tree model is a statistical algorithm, a predictive model built with comparisons of values for a given objective that can be compared with other constant values, placing these variables in a database at hierarchical levels.ObjectivesThis study aims to apply the decision tree model in directing the screening of ADHD complaints to analyze which cognitive and behavioral parameters would be better associations with ADHD accurate diagnosisMethodsWe used a database of research protocol with 202 children assessed with complaints of ADHD and a control group with 185 participants. Decision tree analyzed parameters selected from the cognitive instruments, such voluntary attention, Continuous Performance Test indexes, WCST indexes, Wechsler Intelligence indexes and behavioral scales from CBCL/6-1 and TRF/6-18.ResultsThe highlighted results points to WCST index like: “Perseverative answers” and “Perseverative errors” and “learning to learn” joint to “CPT omissions” and behavioral scales as “CBCL ADHD”, and “CBCL Problems of Attention” produces accuracy of diagnosis discrimination from 84.7% to 60% in the precision of the decision tree.ConclusionsThe decision tree and machine learning approaches can be effective in directing the screening of typical ADHD complaints.DisclosureNo significant relationships.

IntroductionComorbidities between Anxiety Disorders, Depressive Disorders or Somatic Symptoms, and Attention Deficit Hyperactivity Disorder (ADHD) can cause variability in the functional impairments faced by young adults. Knowing the possible configurations resulting from these comorbidities is important for a better understanding of the cases, diagnostic processes, and proposed treatments.ObjectivesTo verify associations between indicators of the aforementioned mental disorders, and symptoms of inattention or hyperactivity-impulsivity, and functional impairments in different areas of life, related to ADHD.MethodsThere were 27 participants (23 women, age m = 22.5 sd = 1.8, education m = 15.7 sd = 2.2), with complaints of inattention and hyperactivity-impulsivity compatible with ADHD, screened with ASRS-18 score> 24 and WASI IQ> 79, and assessed by DIVA-2.0 (symptoms of ADHD), ASR-ASEBA (depressive, anxiety and somatic problems), EPF-ADHD (functional impairments in the academic, professional, affective, domestic, social, health, financial, traffic areas and legal risk). Spearman’s Correlation analysis was performed in the SPSS program (significance p <0.05).ResultsIncrease in depressive problems associated with increased symptoms of inattention (rho=0.386, p=0.049) and hyperactivity-impulsivity (rho = 0.406, p = 0.036). Increased somatic problems associated with increased functional impairment in health (rho=0.458, p=0.016). Increase in depressive problems associated with increased losses in the academic (rho=0.437, p=0.023), affective (rho=0.408, p=0.034), domestic (rho=0.550, p=0.002), social (rho=0.445, p=0.002), financial (rho=0.389, p=0.045) and health (rho=0.514, p=0.006).ConclusionsADHD with comorbidities can have a peculiar clinical evolution with specific characteristics, including diagnosis, management, and response to treatment. These subgroups with different intervention needs demand outlining needs and personalized treatment.DisclosureNo significant relationships.