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Building a Predictive Model of ADHD among Children

Authors

Xiangbo Guo

Rubric:Preventive Medicine
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Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. According to the Centers of Disease Control and Prevention (CDC), the estimated number of children ever diagnosed with ADHD nationalwide is 6.1 million (9.4%). Among those children, 6 in 10 with ADHD had at least one other mental, emotional, or behavioral disorder that may have long-lasting impacts on their development.

 In this research, we investigated possible risk factors related to development of ADHD among children and identified the most significant positive and negative factors through logistic regression. We used the 2020 National Survey of Children’s Health survey data containing 42,777 complete data samples with features ranging from demographic information to the child’s family condition. The response variable is whether a child has ever been diagnosed with ADHD.

After processing the dataset, we built a logistic regression model to predict whether a child will develop ADHD. By investigating the logistic regression coefficients, we found that parents’ physical and mental health, the family’s financial ability to cover basic living expenses, and whether the parents are divorced are all risk factors. The logistic regression model has achieved an AUROC score of 0.73, with 0.67 true positive rate (TPR) and 0.324 false positive rate (FPR). This predictive model is helpful for healthcare professionals to identify and reduce the risk for the children that are prone to the development of ADHD.

Keywords

children
ADHD
logistic regression
ROC
risk
model

References:

[1] NSCH 2003-2011: National Survey of Children’s Health, telephone survey data; estimate includes children 4-17 years of age

[2] Danielson ML, Bitsko RH, Ghandour RM, Holbrook JR, Kogan MD, Blumberg SJ. Prevalence of parent-reported ADHD diagnosis and associated treatment among U.S. children and adolescents, 2016. Journal of Clinical Child and Adolescent Psychology. 2018, 47:2, 199-212.

[3] Banerjee TD, Middleton F, Faraone SV. Environmental risk factors for attention-deficit hyperactivity disorder. Acta Paediatr. 2007 Sep;96(9):1269-74. doi: 10.1111/j.1651-2227.2007.00430.x. PMID: 17718779.

[4] Google. Classification: ROC Curve and AUC  |  Machine Learning Crash Course. Accessed November 25, 2021. https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc.

[5] Nøvik TS, Hervas A, Ralston SJ, et al. Influence of gender on attention-deficit/hyperactivity disorder in Europe–ADORE. Eur Child Adolesc Psychiatry 2006; 15(Suppl 1): I15-I24.

[6] Willcutt EG. The prevalence of DSM-IV attention-deficit/hyperactivity disorder: a meta-analytic review. Neurotherapeutics 2012; 9: 490-499.

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