Special Lecture

Machine Learning Methods for Risk Factor Assessment of Temporomandibular Disorders Based on Nationwide Survey Data: KNHANES IV-3
Yoon-Ji Kim

Lecture Description
This presentation shows how artificial intelligence was used to determine whether biological and psychosocial factors, such as stress, socioeconomic status, and working conditions, were major risk factors for temporomandibular disorders (TMDs). Data were retrieved from the fourth Korea National Health and Nutritional Examination Survey (2009), with information concerning 4,744 participants’ TMDs, demographic factors, socioeconomic status, working conditions, and health-related determinants. Based on variable importance observed from the random forest, the top 20 determinants of self-reported TMDs were body mass index (BMI), household income (monthly), sleep (daily), obesity (subjective), health (subjective), working conditions (control, hygiene, respect, risks, and workload), occupation, education, metropolitan region, residence type (apartment), stress, smoking status, marital status, and sex. The top 20 determinants of temporomandibular disorders determined via a doctor’s diagnosis were BMI, age, household income (monthly), sleep (daily), obesity (subjective), working conditions (control, hygiene, risks, and workload), household income (subjective), subjective health, education, smoking status, residential area type, region (metropolitan), sex, marital status, and allergic rhinitis. The results of the study highlights the importance of obesity, general health, stress, socioeconomic status, and working conditions in the management of TMDs.
Learning objective1
  1. Which factors can we control as clinicians to treat patients with TMDs?