Contraceptive ever-use among women of reproductive age in Northern Ghana: a comparative analysis of logistic regression, LASSO penalised regression, and random forest machine learning approaches.

IF 2.4 Q2 OBSTETRICS & GYNECOLOGY
Frontiers in global women's health Pub Date : 2026-09-09 eCollection Date: 2026-01-01 DOI:10.3389/fgwh.2026.1857008
Joseph Lasong, Mavis Kala, Torjim Salifu, Yula Salifu
{"title":"Contraceptive ever-use among women of reproductive age in Northern Ghana: a comparative analysis of logistic regression, LASSO penalised regression, and random forest machine learning approaches.","authors":"Joseph Lasong, Mavis Kala, Torjim Salifu, Yula Salifu","doi":"10.3389/fgwh.2026.1857008","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Contraceptive uptake in Northern Ghana remains low despite high awareness, suggesting barriers not fully captured by additive models. This study compared logistic regression, LASSO, and Random Forest to predict contraceptive ever-use and identify determinants among women in Tamale Metropolis.</p><p><strong>Methods: </strong>A facility-based cross-sectional survey (January-April 2023) included 414 women aged 15-49 from three public health facilities. Outcome: contraceptive ever-use. Twelve predictors included knowledge/attitude scores, wealth tertile, education, parity, marital status, occupation, age, cultural barriers, health worker attitudes, and information source. Model discrimination was assessed via AUC with 95% CIs; LASSO used 10-fold cross-validation (<i>λ</i>.min=0.012); Random Forest used 500 trees.</p><p><strong>Results: </strong>Random Forest achieved highest discrimination (AUC=0.865, 95% CI:0.83-0.90), outperforming logistic regression (AUC=0.727) and LASSO (AUC=0.723) (both <i>p</i> < 0.001); logistic and LASSO did not differ (<i>p</i> = 0.91). Logistic regression identified five independent predictors: positive attitudes (AOR=1.53), education above primary (AOR=2.72), parity (AOR=2.95), health worker as information source (AOR=2.07), and richest wealth tertile inversely associated (AOR=0.49). Knowledge was non-significant in regression but ranked second in Random Forest importance, suggesting non-linear/interaction effects. LASSO retained nine predictors, dropping cultural barriers, health worker attitudes, and age.</p><p><strong>Conclusions: </strong>Random Forest provided better discrimination and may complement regression for prediction, though regression remains essential for interpretable effect estimates. Attitude change, education, and counselling quality are priority targets. The inverse wealth association warrants qualitative investigation, as polygyny, religiosity, and partner dynamics were unmeasured.</p>","PeriodicalId":73087,"journal":{"name":"Frontiers in global women's health","volume":"7 ","pages":"1857008"},"PeriodicalIF":2.4000,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597874/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in global women's health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fgwh.2026.1857008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Contraceptive uptake in Northern Ghana remains low despite high awareness, suggesting barriers not fully captured by additive models. This study compared logistic regression, LASSO, and Random Forest to predict contraceptive ever-use and identify determinants among women in Tamale Metropolis.

Methods: A facility-based cross-sectional survey (January-April 2023) included 414 women aged 15-49 from three public health facilities. Outcome: contraceptive ever-use. Twelve predictors included knowledge/attitude scores, wealth tertile, education, parity, marital status, occupation, age, cultural barriers, health worker attitudes, and information source. Model discrimination was assessed via AUC with 95% CIs; LASSO used 10-fold cross-validation (λ.min=0.012); Random Forest used 500 trees.

Results: Random Forest achieved highest discrimination (AUC=0.865, 95% CI:0.83-0.90), outperforming logistic regression (AUC=0.727) and LASSO (AUC=0.723) (both p < 0.001); logistic and LASSO did not differ (p = 0.91). Logistic regression identified five independent predictors: positive attitudes (AOR=1.53), education above primary (AOR=2.72), parity (AOR=2.95), health worker as information source (AOR=2.07), and richest wealth tertile inversely associated (AOR=0.49). Knowledge was non-significant in regression but ranked second in Random Forest importance, suggesting non-linear/interaction effects. LASSO retained nine predictors, dropping cultural barriers, health worker attitudes, and age.

Conclusions: Random Forest provided better discrimination and may complement regression for prediction, though regression remains essential for interpretable effect estimates. Attitude change, education, and counselling quality are priority targets. The inverse wealth association warrants qualitative investigation, as polygyny, religiosity, and partner dynamics were unmeasured.

加纳北部育龄妇女避孕药具的使用:逻辑回归、LASSO惩罚回归和随机森林机器学习方法的比较分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
3.70
自引率
0.00%
发文量
0
审稿时长
13 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书