Prediction of menstrual literacy among female university students using tree-based machine learning algorithms: A cross-sectional study in Bangladesh.

ABSTRACT:

Background

Menstrual literacy is crucial for safeguarding the health, dignity, and rights of women. However, in low- and middle-income countries (LMICs), such as Bangladesh, it remains an overlooked issue. Female university students, positioned at a pivotal developmental stage, require accurate and comprehensive knowledge about menstruation. Despite overall improvements in education, significant gaps persist in menstrual knowledge and hygiene practices among this group.

Objectives

This study aimed to predict menstrual literacy levels among female university students in Bangladesh using tree-based machine learning models. It also sought to identify the most influential predictors shaping menstrual knowledge.

Design

A cross-sectional analytical design was employed to as

SUBMITTER: Hassan MM 

PROVIDER: S-EPMC12744004 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature