Version 1
: Received: 29 July 2024 / Approved: 30 July 2024 / Online: 30 July 2024 (13:42:56 CEST)
How to cite:
Maekawa, E.; Martins, M.; Nakamura, C. A.; Araya, R.; Peters, T. J.; Van De Ven, P.; Scazufca, M. Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach. Preprints2024, 2024072441. https://doi.org/10.20944/preprints202407.2441.v1
Maekawa, E.; Martins, M.; Nakamura, C. A.; Araya, R.; Peters, T. J.; Van De Ven, P.; Scazufca, M. Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach. Preprints 2024, 2024072441. https://doi.org/10.20944/preprints202407.2441.v1
Maekawa, E.; Martins, M.; Nakamura, C. A.; Araya, R.; Peters, T. J.; Van De Ven, P.; Scazufca, M. Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach. Preprints2024, 2024072441. https://doi.org/10.20944/preprints202407.2441.v1
APA Style
Maekawa, E., Martins, M., Nakamura, C. A., Araya, R., Peters, T. J., Van De Ven, P., & Scazufca, M. (2024). Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach. Preprints. https://doi.org/10.20944/preprints202407.2441.v1
Chicago/Turabian Style
Maekawa, E., Pepijn Van De Ven and Marcia Scazufca. 2024 "Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach" Preprints. https://doi.org/10.20944/preprints202407.2441.v1
Abstract
Anxiety in older individuals is understudied despite its prevalence. Investigating its occurrence can be challenging, yet understanding the factors influencing its recurrence is important for improved management. This study aimed to model the recurrence of anxiety symptomatology in an older population within a five-month timeframe. Data included baseline socio-demographic and general health information for older adults aged 60 years or older with at least mild depressive symptoms. A Bayesian network model explored the relationship between baseline data and recurrent anxiety symptomatology. Model evaluation employed the Area Under the Receiver Operating Characteristic Curve (AUC). The Bayesian model was also compared to three machine learning models. The model achieved an AUC of 0.821 on the test data, using a threshold of 0.367. This result surpassed both the Logistic Regression and XGBoost models, but slightly behind SGDClassifier. Key factors associated with recurrence of anxiety symptomatology were: “Not being able to stop or control worrying”; “Becoming easily annoyed or irritable”; “Trouble relaxing”; and “depressive symptomatology severity”. The model demonstrated generalisation abilities and outperformed some machine learning models while being less complex and more explainable. These findings indicate a prioritised sequence of predictors to identify individuals most likely to experience recurrent anxiety symptomatology.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
