{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T01:18:50Z","timestamp":1792459130785,"version":"4.5.0"},"reference-count":15,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Penalized logistic regression is widely used in biomedical classification to address multicollinearity and improve predictive performance, yet the stability and reproducibility of selected predictors are often overlooked. This study evaluates feature stability and interpretability in ridge, lasso, and elastic-net logistic regression for breast cancer diagnosis using the Wisconsin Diagnostic Breast Cancer dataset. Models were trained with cross-validated tuning and evaluated on an independent test set using discrimination, classification, and calibration metrics. Feature stability was quantified through bootstrap selection frequencies. All penalized models achieved near-perfect discrimination and improved calibration compared with unpenalized logistic regression. However, substantial differences emerged in stability and sparsity. Ridge regression exhibited maximal stability but retained all predictors, limiting interpretability. Lasso regression produced highly sparse models but showed greater selection variability. Elastic-net regression balanced sparsity and stability, consistently retaining correlated predictors linked to tumor morphology. These findings demonstrate that stability assessment provides critical information beyond predictive accuracy and supports stability-aware penalized modeling for interpretable and reproducible biomedical risk prediction.<\/jats:p>","DOI":"10.1371\/journal.pone.0353489","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T17:35:37Z","timestamp":1785260137000},"page":"e0353489","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Stability and interpretability of penalized logistic regression models for breast cancer risk prediction"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3491-6436","authenticated-orcid":true,"given":"Francis","family":"Okyere","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Nyanney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"issue":"1","key":"pone.0353489.ref001","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","article-title":"Assessing the performance of prediction models: a framework for traditional and novel measures","volume":"21","author":"EW Steyerberg","year":"2010","journal-title":"Epidemiology"},{"issue":"6","key":"pone.0353489.ref002","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1002\/widm.1072","article-title":"Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics","volume":"2","author":"A Boulesteix","year":"2012","journal-title":"WIREs Data Min & Knowl"},{"issue":"1","key":"pone.0353489.ref003","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge Regression: Biased Estimation for Nonorthogonal Problems","volume":"12","author":"AE Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"pone.0353489.ref004","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"R Tibshirani","year":"1996","journal-title":"J R Stat Soc Ser B"},{"key":"pone.0353489.ref005","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"H Zou","year":"2005","journal-title":"J R Stat Soc Ser B"},{"key":"pone.0353489.ref006","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2009"},{"key":"pone.0353489.ref007","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1214\/13-EJS815","article-title":"The lasso problem and uniqueness","volume":"7","author":"R Tibshirani","year":"2013","journal-title":"Electron J Stat"},{"key":"pone.0353489.ref008","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1111\/j.1467-9868.2010.00740.x","article-title":"Stability selection","volume":"72","author":"N Meinshausen","year":"2010","journal-title":"J R Stat Soc Ser B"},{"key":"pone.0353489.ref009","author":"Kaggle","year":"2025"},{"key":"pone.0353489.ref010","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-19425-7","volume-title":"Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis","author":"FE Harrell","year":"2015","edition":"2"},{"key":"pone.0353489.ref011","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3329\/ijss.v24i20.78212","article-title":"Comparison among ordinary least squares, ridge, lasso, and elastic net estimators in the presence of outliers: simulation and application","volume":"24","author":"HM Nayem","year":"2024","journal-title":"Int J Stat Sci"},{"issue":"2","key":"pone.0353489.ref012","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3390\/stats8020045","article-title":"Evaluating Estimator Performance Under Multicollinearity: A Trade-Off Between MSE and Accuracy in Logistic, Lasso, Elastic Net, and Ridge Regression with Varying Penalty Parameters","volume":"8","author":"HM Nayem","year":"2025","journal-title":"Stats"},{"issue":"1","key":"pone.0353489.ref013","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for rating diagnostic tests","volume":"3","author":"WJ Youden","year":"1950","journal-title":"Cancer"},{"issue":"1","key":"pone.0353489.ref014","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"JA Hanley","year":"1982","journal-title":"Radiology"},{"key":"pone.0353489.ref015","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-16399-0","volume-title":"Clinical Prediction Models","author":"EW Steyerberg","year":"2019"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0353489","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T17:35:40Z","timestamp":1785260140000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0353489"}},"subtitle":[],"editor":[{"given":"Zakariya Yahya","family":"Algamal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,28]]},"references-count":15,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7,28]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0353489","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,28]]}}}