Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis.

Journal: Medicina (Kaunas, Lithuania)
PMID:

Abstract

: Recent research has focused on exploring the relationships between various factors associated with headaches and understanding their impact on individuals' psychological states. Utilizing statistical methods and machine learning models, these studies aim to analyze and predict these relationships to develop effective approaches for headache management and prevention. : Analyzing data from 398 patients (train set = 318 and test set = 80), we investigated the influence of various features on outcomes such as depression, anxiety, and headache intensity using machine learning and linear regression. The study employed a mixed-methods approach, combining medical records, interviews, and surveys to gather comprehensive data on participants' experiences with headaches and their associated psychological effects. : Machine learning models, including Random Forest (utilized for Headache Impact Test-6, Patient Health Questionnaire-9, and Generalized Anxiety Disorder-7) and Support Vector Regression (applied to Migraine Disability Assessment), revealed key features contributing to each outcome through Shapley values, while linear regression provided additional insights. Frequent analgesic medication emerged as a significant predictor of poorer life quality (Headache Impact Test-6, root mean squared error = 7.656) and increased depression (Patient Health Questionnaire-9, root mean squared error = 5.07) and anxiety (Generalized Anxiety Disorder-7, root mean squared error = 4.899) in the Random Forest model. However, interpreting the importance of features in complex models like supportive vector regression poses challenges, and determining causality between factors such as medication usage and pain severity was not feasible. : Our study underscores the importance of considering individual characteristics in optimizing treatment strategies for headache patients.

Authors

  • Jong-Ho Kim
    Korea University Research Institute for Medical Bigdata Science, Korea University, Seoul, Korea.
  • Hye-Sook Kim
    Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea.
  • Jong-Hee Sohn
    Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea.
  • Sung-Mi Hwang
    Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea.
  • Jae-Jun Lee
    Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea.
  • Young-Suk Kwon
    Division of Big Data and Artificial Intelligence, Institute of New Frontier Research, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea. gettys79@gmail.com.