A bagging ensemble machine learning method for imbalanced data to predict anxiety disorders and analyze risk factors in older people - An observational study

Aug 5, 2026

Anxiety disorders are among the most prevalent mental health problems, particularly in older adults, creating a strong need for accurate prediction and effective care. This observational study develops an adapted bagging ensemble machine learning system to support diagnosis and forecasting while addressing extremely imbalanced data from the Trinity-Ulster-Department of Agriculture study. Statistical analyses identify risk factors, and biomarker-driven feature selection and engineering are applied. Five machine learning models build weak learner submodels, producing promising predictive performance, and the study reports several risk factors to inform future early prediction research.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Abstract

Keywords

Introduction

A bagging ensemble machine learning method for imbalanced data to predict anxiety disorders and analyze risk factors in older people: An observational study Wang, J. , Black, M. , Rankin, D. , Wallace, J. G. , Hughes, C. , Hoey, L. , Moore, AJ. , Tobin, J. , Zhang, M. , Ng, J. , Horigan, G. E. A. , Carlin, P. , McCarroll, K. , Cunningham, C. , McNulty, H. , & Molloy, A. (2026) . A bagging ensemble machine learning method for imbalanced data to predict anxiety disorders and analyze risk factors in older people: An observational study. Artificial Intelligence in Health, 3(1), 116-137. [https://doi.org/10.36922/AIH025070009](https://doi.org/10.36922/AIH025070009) Link to publication record in Ulster University Research Portal Published in: Artificial Intelligence in Health Publication Status: Published (in print/issue): 14/01/2026 DOI: 10.36922/AIH025070009 Document Version Publisher's PDF, also known as Version of record Document Licence: CC BY For Author Accepted Manuscripts (AAM) published under Ulster University's Rights Retention Policy for Scholarly Works (RRPSW) When citing an AAM published under Ulster University's RRPSW please use the following citation structure: Author, A. A. (Year) . Title of article. Journal Name,[Accepted Author Manuscript] . PURE Portal URL. Licensed under CC BY 4.0. General rights The copyright and moral rights to the output are retained by the output author(s), unless otherwise stated by the document licence. Unless otherwise stated, users are permitted to download a copy of the output for personal study or non-commercial research and are permitted to freely distribute the URL of the output. They are not permitted to alter, reproduce, distribute or make any commercial use of the output without obtaining the permission of the author(s) . If the document is licenced under Creative Commons, the rights of users of the documents can be found at [https://creativecommons.org/share-your-work/cclicenses/](https://creativecommons.org/share-your-work/cclicenses/) . Take down policy The Research Portal is Ulster University's institutional repository that provides access to Ulster's research outputs. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [pure-support@ulster.ac.uk](pure-support@ulster.ac.uk) Download date: 04/08/2026 Artificial Intelligence in Health *Corresponding author: Jinling Wang ([j.wang@ulster.ac.uk](j.wang@ulster.ac.uk)) Citation: Wang J, Black M, Rankin D, et al. A bagging ensemble machine learning method for imbalanced data to predict anxiety disorders and analyze risk factors in older people: An observational study. Artif Intell Health. doi: 10.36922/AIH025070009 Received: February 12, 2025 1st revised: June 27, 2025 2nd revised: July 7, 2025 Accepted: July 14, 2025 Published online: September 8, 2025 Copyright: © 2025 Author(s) . This is an Open-Access article distributed under the terms of the Creative Commons Attribution License, permitting distribution, and reproduction in any medium, provided the original work is properly cited. Publisher’s Note: AccScience Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ORIGINAL RESEARCH ARTICLE A bagging ensemble machine learning method for imbalanced data to predict anxiety disordersand analyze risk factors in older people: An observational study Jinling Wang1*, Michaela Black1, Debbie Rankin1, Jonathan Wallace2, Catherine F. Hughes3, Leane Hoey3, Adrian Moore4, Joshua Tobin5, Mimi Zhang5, James Ng5, Geraldine Horigan3, Paul Carlin6, Kevin McCarroll7, Conal Cunningham7, Helene McNulty3, and Anne M. Molloy8 1School of Computing, Engineering and Intelligent Systems, Ulster University, Derry-Londonde

FAQs

What problem does the study address?

The study targets anxiety disorders in older people and the need for effective diagnosis and prediction despite extremely imbalanced data.

What modeling approach does the study use?

It develops an adapted bagging ensemble machine learning system that builds weak learner submodels using five machine learning methods.

How are risk factors identified?

Risk factors are identified using statistical techniques, supported by feature selection and feature engineering based on biomarker risk factors.

Aug 5, 2026

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