Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis
<i>Background and Objectives</i>: 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...
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MDPI AG
2025-01-01
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| author | Jong-Ho Kim Hye-Sook Kim Jong-Hee Sohn Sung-Mi Hwang Jae-Jun Lee Young-Suk Kwon |
| author_facet | Jong-Ho Kim Hye-Sook Kim Jong-Hee Sohn Sung-Mi Hwang Jae-Jun Lee Young-Suk Kwon |
| author_sort | Jong-Ho Kim |
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| description | <i>Background and Objectives</i>: 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. <i>Materials and Methods</i>: 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. <i>Results</i>: 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. <i>Conclusions</i>: Our study underscores the importance of considering individual characteristics in optimizing treatment strategies for headache patients. |
| format | Article |
| id | doaj-art-bc222bdbc8c249d4b427fb2ae8a64ded |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Medicina |
| spelling | doaj-art-bc222bdbc8c249d4b427fb2ae8a64ded2025-08-20T03:12:19ZengMDPI AGMedicina1010-660X1648-91442025-01-0161218810.3390/medicina61020188Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning AnalysisJong-Ho Kim0Hye-Sook Kim1Jong-Hee Sohn2Sung-Mi Hwang3Jae-Jun Lee4Young-Suk Kwon5Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of KoreaInstitute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Republic of KoreaInstitute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Republic of KoreaDepartment of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of KoreaDepartment of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of KoreaDepartment of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea<i>Background and Objectives</i>: 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. <i>Materials and Methods</i>: 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. <i>Results</i>: 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. <i>Conclusions</i>: Our study underscores the importance of considering individual characteristics in optimizing treatment strategies for headache patients.https://www.mdpi.com/1648-9144/61/2/188headachepain intensitydepressionanxietyfunctional disabilitymachine learning |
| spellingShingle | Jong-Ho Kim Hye-Sook Kim Jong-Hee Sohn Sung-Mi Hwang Jae-Jun Lee Young-Suk Kwon Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis Medicina headache pain intensity depression anxiety functional disability machine learning |
| title | Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis |
| title_full | Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis |
| title_fullStr | Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis |
| title_full_unstemmed | Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis |
| title_short | Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis |
| title_sort | functional disability and psychological impact in headache patients a comparative study using conventional statistics and machine learning analysis |
| topic | headache pain intensity depression anxiety functional disability machine learning |
| url | https://www.mdpi.com/1648-9144/61/2/188 |
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