Association between maternal overprotection and premenstrual disorder: a machine learning based exploratory study.

ABSTRACT:

Background

Premenstrual disorder (PMD), which includes premenstrual syndrome and premenstrual dysphoric disorder, has a complex pathogenesis and may be closely related to emotional cognition and memory. However, the mechanisms underlying these associations remain unclear. Therefore, this study used machine learning to explore the roles of various factors that are not typically considered risk-factors for PMD.

Methods

A predictive model for PMD was constructed using a dataset of questionnaire responses and heartrate variability data collected from 60 participants during their follicular and luteal phases. Based on the Japanese version of the Premenstrual Symptom Screening Tool, the binary objective variable (PMD status) was defined as "PMD" for moderate-to-severe premenstrual

SUBMITTER: Tsuyuki K 

PROVIDER: S-EPMC11849209 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature