Identifying predictors of formal help-seeking for premenstrual symptoms: A machine learning analysis of symptom, functional impairment and barriers data

Aug 5, 2026

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Despite the potential severity and burden of premenstrual symptoms, formal care is often not sought. The study applies machine learning to identify which symptom profiles, functional impairment, and perceived barriers predict whether individuals seek help for premenstrual symptoms. Using a UK online survey dataset, descriptive statistics and group comparisons were conducted, then decision-tree models with Extreme Gradient Boosting (XGBoost) built predictive performance estimates. Among 592 participants with complete consecutive-cycle data, the model showed fair discrimination and highlighted key predictors linked to impaired social and work functioning, perceived severity, and prior poor care experiences.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Introduction

Background on premenstrual symptoms

Clinical relevance and impaired functioning

OPEN ACCESS Citation: Funnell EL, Martin-Key NA, Tomasik J, Bahn S (2025) Identifying predictors of formal help-seeking for premenstrual symptoms: A machine learning analysis of symptom, functional impairment and barriers data. PLOSMent Health 2(8): e0000274. [https://doi](https://doi). org/10.1371/journal.pmen.0000274 Editor: Ariel Soares Teles, Instituto Federal do Maranhão: Instituto Federal de Educacao Ciencia e Tecnologia do Maranhão, BRAZIL Received: February 3, 2025 Accepted: June 22, 2025 Published: August 27, 2025 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pmen.0000274 Copyright: © 2025 Funnell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, RESEARCH ARTICLE Identifying predictors of formal help-seeking for premenstrual symptoms: A machine learning analysis of symptom, functional impairment and barriers data Erin L. Funnell, Nayra A. Martin-Key, Jakub Tomasik, Sabine Bahn* Cambridge Centre for Neuropsychiatric Research (CCNR), Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom * [sb209@cam.ac.uk](sb209@cam.ac.uk) Abstract Despite the potential severity and burden of premenstrual symptoms, few appear to seek formal care. Given that access to many therapeutic interventions requires formal help-seeking, it is important to understand predictors of this health behaviour. This study employed machine learning to identify symptoms, functional impairment, and barriers to accessing care that predict formal help-seeking for premenstrual symptoms. Data was collected from a UK-based sample using online survey software and explored using descriptive analysis. Group differences in ordinal and categorical data between those who have and have not sought formal help specifically for premenstrual symptoms were examined using Mann-Whitney U tests and Chi-square tests, respectively. Predictive models of help-seeking were built using the decision tree-based machine learning method, Extreme Gradient Boosting (XGBoost) . A total of 592 participants with complete data who endorsed premenstrual symptoms in consecutive cycles were included for analysis. Of those, 57.26%(n = 339) had previously seen a healthcare professional specifically for premenstrual symptoms. The model predicting formal help-seeking demonstrated fair performance, with an area under the receiver operating characteristic curve (AUROC) of 0.75 (SD = 0.06), a sensitivity of 0.65 (SD = 0. 10), and a specificity of 0.71 (SD = 0. 11) . The strongest predictors of formal help-seeking were impaired social functioning, thinking that symptoms were severe, impairment in work/studies, and a previous poor care experience for gynaecological/reproductive conditions. These insights may be leveraged to encourage help-seeking behaviour, potentially reducing unnecessary distress or impairment. Improved knowledge of premenstrual symptoms and disorders is vital to facilitate identification of severe symptoms and impaired functioning related to the menstrual cycle. Additionally, improved guidance on when to seek help is required to increase the rate of formal help-seeking, particularly for individuals with high-risk psychological symptoms such as suicidality. More work is needed to determine the specific PLOS Mental Health | [https://doi.org/10.1371/journal.pmen.0000274](https://doi.org/10.1371/journal.pmen.0000274) August 27, 2025 1 / 13 which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data availability statement: Due to the restrictions on data access specified i

FAQs

Why is it important to study predictors of formal help-seeking for premenstrual symptoms?

Formal care is needed for access to many therapies, yet few people seek it. Understanding predictors can support interventions that encourage timely help-seeking and reduce distress or impairment.

How was the study designed and analyzed?

Data came from a UK-based online survey and were examined with descriptive and group comparison tests. Predictive modeling used decision-tree based Extreme Gradient Boosting (XGBoost) to estimate which variables relate to help-seeking.

Which factors were identified as strongest predictors of formal help-seeking?

The strongest predictors included impaired social functioning, believing symptoms were severe, impairment in work/studies, and a previous poor care experience for gynaecological or reproductive conditions.

Aug 5, 2026

Upload by Quinn