{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T18:13:52Z","timestamp":1755800032438,"version":"3.44.0"},"reference-count":30,"publisher":"The Korean Society of Medical Informatics","issue":"3","license":[{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["2022RIS005"],"award-info":[{"award-number":["2022RIS005"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Institute of Information & communications Technology Planning & Evaluation","award":["2019-0-01219"],"award-info":[{"award-number":["2019-0-01219"]}]}],"content-domain":{"domain":["e-hir.org"],"crossmark-restriction":true},"short-container-title":["Healthc Inform Res"],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>Objectives: The objective of this study was to evaluate the effectiveness of machine learning (ML) models using selected subsets of features to predict age based on intracranial arterial segments\u2019 tortuosity and diameter characteristics derived from magnetic resonance angiography (MRA) data. Additionally, this study aimed to identify key vascular features important for predicting vascular age. Methods: Three-dimensional time-of-flight MRA image data from 171 subjects were analyzed. After annotating the endpoints for each arterial segment, 169 features\u2014comprising tortuosity metrics and arterial segment diameter statistics\u2014were extracted. Five ML models (random forest, linear regression, AdaBoost, XGBoost, and lightGBM) were trained and validated. Two feature selection methods, correlation-based feature selection (CFS) and Relief-F, were applied to identify optimal feature subsets. Results: The random forest model utilizing the CFS-based 50% feature subset achieved the best performance, with a root mean square error of 14.0 years, a coefficient of determination (R2) of 0.275, and a Pearson correlation coefficient of 0.560. Tortuosity metrics (e.g., triangular index of the left posterior cerebral artery P1 segment) appeared more frequently than diameter statistics among the top five most important features. Conclusions: CFS-based feature selection enhanced the performance of ML-based age prediction compared with using the complete feature set. Linear regression consistently demonstrated the poorest performance across all evaluation metrics. ML-based age prediction using segmental tortuosity metrics and diameter statistics is feasible, potentially revealing significant features related to vascular aging.<\/jats:p>","DOI":"10.4258\/hir.2025.31.3.284","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:49:25Z","timestamp":1754614165000},"page":"284-294","update-policy":"https:\/\/doi.org\/10.4258\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based Age Prediction with Feature Subset Selection from Magnetic Resonance Angiography Data"],"prefix":"10.4258","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1481-7723","authenticated-orcid":true,"given":"Hoon-Seok","family":"Yoon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5012-0268","authenticated-orcid":true,"given":"Yoon-Chul","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2780","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.14336\/AD.2017.0430"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCRESAHA.118.312563"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2018.06.017"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.4564"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.nic.2022.04.010"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/jcbfm.2012.18"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2008.03.022"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2019.12.008"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.25805"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1161\/HYPERTENSIONAHA.118.11647"},{"volume-title":"","year":"2011","author":"Diedrich","key":"ref11"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1097\/HCO.0000000000000218"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci13111512"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/tomography9040113"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2462750"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.453"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging10030058"},{"key":"ref18","first-page":"2825","volume-title":"Scikit-learn: machine learning in Python","author":"Pedregosa","year":"2011"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/jcm8020237"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-0772-5"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103375"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1023\/A:1025667309714"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-57868-4_57"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref25","first-page":"3146","volume-title":"LightGBM: a highly efficient gradient boosting decision tree","author":"Ke","year":"2017"},{"key":"ref26","first-page":"281","volume-title":"Random search for hyper-parameter optimization","author":"Bergstra","year":"2012"},{"key":"ref27","first-page":"4765","volume-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2019.105405"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2020.110927"},{"volume-title":"","year":"2018","author":"Tan","key":"ref30"}],"container-title":["Healthcare Informatics Research"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/e-hir.org\/upload\/pdf\/hir-2025-31-3-284.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/e-hir.org\/journal\/view.php?doi=10.4258\/hir.2025.31.3.284","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/e-hir.org\/upload\/pdf\/hir-2025-31-3-284.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T06:12:43Z","timestamp":1755670363000},"score":1,"resource":{"primary":{"URL":"http:\/\/e-hir.org\/journal\/view.php?doi=10.4258\/hir.2025.31.3.284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,31]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,7]]}},"URL":"https:\/\/doi.org\/10.4258\/hir.2025.31.3.284","relation":{},"ISSN":["2093-369X"],"issn-type":[{"type":"electronic","value":"2093-369X"}],"subject":[],"published":{"date-parts":[[2025,7,31]]},"assertion":[{"value":"2024-05-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-15","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-31","order":2,"name":"published","label":"Published online","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-31","order":3,"name":"published","label":"Print publication","group":{"name":"publication_history","label":"Publication History"}}]}}