{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:56:03Z","timestamp":1785419763334,"version":"3.56.0"},"reference-count":20,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T00:00:00Z","timestamp":1735084800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      One of the main causes of infertility\n                      \n                      \n                      \n                      among women nowadays is Polycystic Ovarian Syndrome, or PCOS.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>Objectives:<\/jats:bold>\n                      A decision support strategy for supporting medical specialists through PCOS monitoring is presented in the suggested work. A feature selection model that is based on wrapper categorization is used in this work. The performance of the classifier may be impacted by the existence of redundant features.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>Method:<\/jats:bold>\n                      In order to address these issues, the PCOS is classified using the Machine Learning (ML) Extreme Gradient Boosting (XGBoost) classifier, while the best features are found using the Adaptive Tunicate Search Algorithm (ATSA). By using the number of optimized features as the fitness function, the suggested ATSA increases the capabilities for exploration and exploitation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using the PCOS dataset from the Kaggle repository, the experimental demonstration produced better accuracy and precision than the traditional feature selection methods, achieving 97.5% and 95.7%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The suggested ATSA\u2009+\u2009XGB model's scalability was demonstrated by all experimental analyses.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329241296357","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T02:26:50Z","timestamp":1750818410000},"page":"1478-1488","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Development of a machine learning model to classify polycystic ovarian syndrome"],"prefix":"10.1177","volume":"33","author":[{"given":"Oviya Graselin","family":"S","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arunprasath","family":"T","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pallikonda Rajasekaran","family":"M","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramalakshmi","family":"R","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kottaimalai","family":"R","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex Michael Raj","family":"J","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,12,25]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-21724-0"},{"key":"e_1_3_2_3_2","volume-title":"Detection of polycystic ovary syndrome using machine learning algorithms","author":"Bhat AS","year":"2021","unstructured":"Bhat AS. Detection of polycystic ovary syndrome using machine learning algorithms. Dublin: National College of Ireland, 2021."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12553-021-00613-y"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/IICETA54559.2022.9888677"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119532"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13198-021-01203-x"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e14518"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","unstructured":"Zad Z Jiang VS Wolf AT et al. Predicting polycystic ovary syndrome (PCOS) with machine learning algorithms from electronic health records: medRxiv [Preprint]. 2023 Oct 1:2023.07.27.23293255. DOI: 10.1101\/2023.07.27.23293255.","DOI":"10.1101\/2023.07.27.23293255"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.2174\/1872212115999201224130204"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCNT51525.2021.9580143"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117592"},{"key":"e_1_3_2_13_2","first-page":"77","article-title":"The detection of PCOS using machine learning algorithms and feature selection by K-means clustering","volume":"13","author":"Faris N","year":"2023","unstructured":"Faris N, Miften FS. The detection of PCOS using machine learning algorithms and feature selection by K-means clustering. J Educ Pure Sci-Univ Thi-Qar 2023; 13: 77\u201384.","journal-title":"J Educ Pure Sci-Univ Thi-Qar"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2022.3210256"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TENSYMP54529.2022.9864439"},{"key":"e_1_3_2_16_2","first-page":"246","article-title":"Detection of polycystic syndrome in ovary using machine learning algorithm","volume":"11","author":"Alagarsamy M","year":"2023","unstructured":"Alagarsamy M, Shanmugam N, Paramathi Mani D, et al. Detection of polycystic syndrome in ovary using machine learning algorithm. Int J Intell Syst Appl Eng 2023; 11: 246\u2013253.","journal-title":"Int J Intell Syst Appl Eng"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2022.100102"},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Marreiros M Ferreira D Neto C et al. Classification of Polycystic Ovary Syndrome Based on Correlation Weight Using Machine Learning 2022 pp.150\u2013176.","DOI":"10.4018\/978-1-7998-9172-7.ch006"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3205587"},{"key":"e_1_3_2_20_2","unstructured":"Kottarathil P. Polycystic ovraian syndrome (PCOS) dataset. 2020."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103541"}],"container-title":["Technology and Health Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09287329241296357","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/09287329241296357","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09287329241296357","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T11:08:18Z","timestamp":1785236898000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/09287329241296357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,25]]},"references-count":20,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["10.1177\/09287329241296357"],"URL":"https:\/\/doi.org\/10.1177\/09287329241296357","relation":{},"ISSN":["0928-7329","1878-7401"],"issn-type":[{"value":"0928-7329","type":"print"},{"value":"1878-7401","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,25]]}}}