{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T11:49:19Z","timestamp":1782560959759,"version":"3.54.5"},"reference-count":37,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Australas J Ageing"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Accurate estimation of skeletal muscle mass is a key component in the screening for sarcopenia. Anthropometric parameters provide a non\u2010invasive, cost\u2010effective alternative, yet their predictive capability can be enhanced with machine learning. This study aimed to develop and evaluate machine learning models to predict skeletal muscle mass measured by bioelectrical impedance analysis (BIA) in individuals aged 70\u2009years and older using anthropometric measurements and demographic data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Anthropometric data from 984 older adults aged 70\u2009years and older were obtained from the 8th Size Korea Human Body Measurement Survey. Data preprocessing included removing missing values, applying Min\u2010Max scaling and selecting relevant features. Machine learning models were developed, optimised using a tuning framework and evaluated through error metrics, residual analysis and feature importance interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among the machine learning models, XGBoost achieved the highest predictive accuracy (RMSE\u2009=\u20091.29,\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.92) while using the fewest predictors (18), demonstrating efficiency and effectiveness. Key features included calf circumference, gender, weight, height and waist circumference. SHapley Additive exPlanations analysis emphasised the dominant role of calf circumference, reinforcing its predictive capability for skeletal muscle mass.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study highlights the potential of machine learning for predicting skeletal muscle mass using non\u2010invasive anthropometric measurements. These findings support the proposed model as a scalable and cost\u2010effective tool for estimating skeletal muscle mass, which is a key quantitative component of sarcopenia screening.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ajag.70167","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T13:04:11Z","timestamp":1776863051000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Skeletal Muscle Mass Measured by Bioelectrical Impedance Analysis in Older Adults Using Anthropometric Data"],"prefix":"10.1111","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0498-7207","authenticated-orcid":false,"given":"Jun\u2010hee","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Physical Therapy, College of Software and Digital Healthcare Convergence Yonsei University  Wonju South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-physiol-020518-114310"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2016.04.019"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1017\/S2040174414000452"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjcard.2016.01.033"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.3904\/kjm.2013.85.2.167"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2019.12.012"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/ageing\/afae052"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/ageing\/afy169"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40520-014-0281-4"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.2147\/CIA.S149232"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/5584_2017_18"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/2049-3258-72-45"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06573-2"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40520-016-0589-3"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/rpd\/nci741"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/174_2012_612"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2016.04.004"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1097\/MCO.0000000000000496"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1139\/apnm-2012-0129"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.3390\/nu10060738"},{"key":"e_1_2_11_22_1","first-page":"269","article-title":"Anthropometric Indicators as Screening Tools for Sarcopenia in Older Adult Women","volume":"30","author":"Pinheiro P. 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