Comorbidity Study of Attention-deficit Hyperactivity Disorder (ADHD) in Children: Applying Association Rule Mining (ARM) to Korean National Health Insurance Data

  • Leejin KIM Dept. of Child Studies, Chonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do, South Korea
  • Sungmin MYOUNG Dept. of Health Administration, Jungwon University, 85 Munmu-ro Goesan-eup Goesan-gun, Chungbuk, South Korea
Keywords: ADHD, Association rule mining, Comorbidity, Data mining


Background: The aim of this study was to explore the comorbidity of Attention-Deficit Hyperactivity Disorder (ADHD) for the Korean national health insurance data (NHID) by using association rule mining (ARM).Methods: We used data categorized mental disorder according to the international classification of disease, 10th revision (ICD-10) diagnosis system from NHID from 2011 to 2013 in youths aged 18 yr or younger. Overall, 211420 subjects, comorbid cases with ADHD were present in 105784. ARM was applied to the Apriori algorithm to examine the strengths of associations among those diagnosed, and logistic regression was used to evaluate the relations among rules.Results: The most prevalent comorbid psychiatric disorder of ADHD youths was mood/affective disorders. From results of ARM, nine association rules (support≥1%, confidnce≥50%) were produced. The highest association was found between specific developmental disorders of scholastic skills and ADHD. Among association of three comorbid diseases, tic disorder was an important role in the association between ADHD and other comorbid diseases through results of ARM and logistic regression.Conclusion: The practical application of ARM for discovering the comorbidity of ADHD in large amount real-data such as the Korean NHID was mostly confirmed by past studies. The results of this study will be helpful to researchers evaluating the stability of their diagnosis in ADHD. 


Johnston C, Mash EJ (2001). Families of children with attention-deficit/hyperactivity disorder: review and recommendations for future research. Clin Child Fam Psychol Rev, 4 (3): 183-207.

Mangeot SD, Miller LJ, McIntosh DN et al (2001). Sensory modulation dysfunction in children with attention-deficit–hyperactivity disorder. Dev Med Child Neurol, 43 (6): 399-406.

American Psychiatric Association (1994). Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). American Psychiatric Association, Washington, DC, pp.: 11-137.

Weiss G, Hechtman L, Milroy T et al (1985). Psychi-atric status of hyperactives as adults: a controlled prospective 15-year follow-up of 63 hyperactive children. J Am Acad Child Psychiatry, 24 (2): 211-220.

Srinath S, Girimaji SC, Gururaj G et al (2005). Epi-demiological study of child & adolescent psychiat-ric disorders in urban & rural areas of Bangalore, India. Indian J Med Res, 122 (1): 67-79.

Alyahri A, Goodman R (2008). The prevalence of DSM-IV psychiatric disorders among 7-10 year old Yemeni schoolchildren. Soc Psychiatry Psychiatr Epidemiol, 43 (3): 224-230.

Yang SJ, Cheong S, Hong SD (2006). Prevalence and correlates of attention-deficit hyperactivity disorder: school-based mental health services in Seoul. J Ko-rean Neuropsychiatr Assoc, 45 (1): 69-76.

Park S, Kim BN, Cho SC et al (2015). Prevalence, correlates, and comorbidities of DSM-IV psychi-atric disorders in children in Seoul, Korea. Asia Pac J Public Health, 27 (2): NP1942-NP1951.

Ishii T, Takahashi O, Kawamura Y et al (2003). Comorbidity in attention deficit-hyperactivity dis-order. Psychiatry Clin Neurosci, 57 (5): 457-463.

Yoshida Y, Uchiyama T (2004). The clinical necessity for assessing attention deficit/hyperactivity disor-der (AD/HD) symptoms in children with high-functioning pervasive developmental disorder (PDD). Eur Child Adolesc Psychiatry, 13 (5): 307-314.

Pliszka SR, Carlson CL, Swanson JM (1999). ADHD with comorbid disorders: clinical assessment and management. Guilford Press, New York, pp.: 20-85.

Tai YM, Chiu HW (2009). Comorbidity study of ADHD: applying association rule mining (ARM) to national health insurance database of Taiwan. Int J Med Inform, 78 (12): e75-e83.

Hornik K, Buchta C, Zeileis A (2009). Open-source machine learning: R meets Weka. Comput Stat, 24 (2): 225-232.

Park SH, Jang SY, Kim H et al (2014). An association rule mining-based framework for understand life-style risk behaviors. PloS One, 9 (2): e88859.

Burton SH, Morris RG, Giraud-Carrier CG (2014). Mining useful association rules from questionnaire data. Intelligent Data Analysis, 18 (3): 479-494.

Creightion C, Hanash S (2003). Mining gene expres-sion databases for association rules. Bioinformatics, 19 (1): 79-86.

Nahar J, Tickle KS, Ali AS et al (2011). Significant cancer prevention factor extraction: an association rule discovery approach. J Med Syst, 35 (3): 353-367.

Song SO, Jung CH, Song YD et al (2014). Back-ground and data configuration process of a na-tionwide population-based study using the Kore-an national health insurance system. Diabetes Metab J, 38 (5): 395-403.

Jensen PS, Martin D, Cantwell DP (1997). Comor-bidity in ADHD: implications for research, prac-tice, and DSM-V. J Am Acad Child Adolesc Psychiatry, 36 (8): 1065-1079.

Biederman J, Newcorn J, Sprich S (1991). Comor-bidity of attention deficit hyperactivity disorder with conduct, depressive, anxiety, and other dis-orders. Am J Psychiatry, 148 (5): 564-577.

Agrawal R, Srikant R (1994). Fast algorithms for min-ing association rules in large databases. Proceed-ings of the 20th International Conference on Vary Large Data Bases. Available from:

Stilou S, Bamidis PD, Maglaveras N et al (2001). Mining association rules from clinical databases: an intelligent diagnostic process in healthcare. Stud Health Technol Inform, 84 (2): 1399-1403.

Bellazzi R, Zupan B (2008). Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform, 77 (2): 81-97.

Susser E, Schwartz S, Morabia A et al (2006). Psychiat-ric epidemiology: searching for the causes of mental disorders. Oxford University Press, New York, pp.: 33-75.

How to Cite
KIM L, MYOUNG S. Comorbidity Study of Attention-deficit Hyperactivity Disorder (ADHD) in Children: Applying Association Rule Mining (ARM) to Korean National Health Insurance Data. Iran J Public Health. 47(4):481-488.
Original Article(s)