{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T07:03:12Z","timestamp":1779951792390,"version":"3.53.1"},"reference-count":18,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Reprod. Health"],"abstract":"<jats:p>\n                    Assisted Reproductive Techniques (ART) refers to the reproductive measures that address the issues like infertility, low sperm count, unable to conceive and help the couples to achieve pregnancy. Various Assisted Reproductive techniques like\n                    <jats:italic>in-vitro<\/jats:italic>\n                    Fertilization, Intrauterine Insemination are used to treat patients with infertility. This paper studies the demographic profile of women in view of Assisted Reproductive Techniques through Machine Learning Models. Artificial Intelligence is a vast field that has proved its worth in almost all areas. Machine learning is a branch of AI where computers learn from the data provided and improve their performance by learning from the data provided. Demographic profile of Women is important to study from the view of ART methods as this would make machine understand whether the women is healthy or not. She needs to change her lifestyle or opt for Assisted Reproductive Techniques to achieve a successful conception. In this paper, Machine Learning models are applied on Primary data i.e. Data is collected from women of age 25\u201360 and profile of women is studied to achieve the healthy and non-healthy status of women. The study evaluates Logistic Regression, Decision Tree and Linear Discriminant Analysis, demonstrating that the hybrid approach achieves the highest accuracy (for Healthy Women: F1 score is: 0.80, Precision is: 1.00 and recall is 0.67), (for women with health issue F1 score is 0.93, Precision is 0.88 and Recall is 1.00). Further the study highlights the importance of women health and important factors that decide whether women need Assisted Reproductive Methods to achieve conception or not.\n                  <\/jats:p>","DOI":"10.3389\/frph.2026.1773150","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T06:20:47Z","timestamp":1779949247000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A hybrid model to study the demographic profile of women in view of assisted reproductive techniques through machine learning models"],"prefix":"10.3389","volume":"8","author":[{"family":"Latika","sequence":"first","affiliation":[{"name":"Department of CSE, Jagannath University","place":["Bahadurgarh, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renuka","family":"Arora","sequence":"additional","affiliation":[{"name":"Department of CSE, Jagannath University","place":["Bahadurgarh, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.fertnstert.2024.02.024","article-title":"Prediction of pregnancy-related complications in women undergoing assisted reproduction using machine learning methods","volume":"122","author":"Chen","year":"2024","journal-title":"Fertil Steril"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4108\/eetpht.10.5511","article-title":"Machine learning based assessment and predictive analysis of in-vitro fertilization success rate","volume":"10","author":"Mehta","year":"2024","journal-title":"EAI Endorsed Trans Pervasive Health Technol"},{"key":"B3","first-page":"900","article-title":"Clinical prediction of female infertility through advanced machine learning techniques","volume":"6","author":"Khan","year":"2024","journal-title":"Int J Innov Sci Technol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"104302","DOI":"10.1016\/j.jtherbio.2025.104302","article-title":"Heat stress and human reproduction: effects and pathogenesis","volume":"134","author":"Akhigbe","year":"2025","journal-title":"J Therm Biol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"e68221","DOI":"10.7759\/cureus.68221","article-title":"Impact of climate change on reproductive health and pregnancy outcomes: a systematic review","volume":"16","author":"Papadiochou","year":"2024","journal-title":"Cureus"},{"key":"B6","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s43043-025-00258-1","article-title":"Impact of electromagnetic field exposure on reproductive health: an umbrella review and meta-analysis of systematic reviews","volume":"30","author":"Lamzouri","year":"2025","journal-title":"Middle East Fertil Soc J"},{"key":"B7","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.ogc.2014.09.005","article-title":"Age-related infertility","volume":"42","author":"Crawford","year":"2015","journal-title":"Obstet Gynecol Clin North Am"},{"key":"B8","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1093\/bmb\/ldy023","article-title":"Being a good egg in the 21st century","volume":"127","author":"Anderson","year":"2018","journal-title":"Br Med Bull"},{"key":"B9","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1186\/s12884-024-06398-y","article-title":"The relationship between duration of infertility and clinical outcomes of intrauterine insemination for younger women: a retrospective clinical study","volume":"24","author":"Huang","year":"2024","journal-title":"BMC Pregnancy Childbirth"},{"key":"B10","first-page":"399","article-title":"Ovarian reserve, female age and the chance for successful pregnancy","volume":"55","author":"Toner","year":"2003","journal-title":"Minerva Ginecol"},{"key":"B11","article-title":"Artificial intelligence at assisted reproductive technology","author":"Raimundo","year":"2020"},{"key":"B12","doi-asserted-by":"publisher","first-page":"61","DOI":"10.5005\/jp-journals-10016-1208","article-title":"Artificial intelligence in assisted reproductive technology: present and future","volume":"11","author":"Dala","year":"2020","journal-title":"Int J Infertil Fetal Med"},{"key":"B13","first-page":"C29","volume-title":"Does Artificial Intelligence Have a Role in the IVF Clinic? Reprod Fertil","author":"Chow","year":"2021"},{"key":"B14","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/s41746-024-01006-x","article-title":"The prospect of artificial intelligence to personalize assisted reproductive technology","volume":"7","author":"Hanassab","year":"2024","journal-title":"NPJ Dig Med"},{"key":"B15","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1093\/humupd\/dmaa047","article-title":"IVF and other ART in low- and middle-income countries: a systematic landscape analysis","volume":"27","author":"Chiware","year":"2021","journal-title":"Hum Reprod Update"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1007\/s43032-022-00941-y","article-title":"Automation in ART: paving the way for the future of infertility treatment","volume":"30","author":"Abdullah","year":"2023","journal-title":"Reprod Sci"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1007\/s10815-021-02254-6","article-title":"Embryo selection with artificial intelligence: how to evaluate and compare methods?","volume":"38","author":"Kragh","year":"2021","journal-title":"J Assist Reprod Genet"},{"key":"B18","doi-asserted-by":"publisher","first-page":"4985","DOI":"10.3390\/ijerph17144985","article-title":"Coital frequency and the probability of pregnancy in couples trying to conceive their first child: a prospective cohort study in Japan","volume":"17","author":"Konishi","year":"","journal-title":"Int J Environ Res Public Health"}],"container-title":["Frontiers in Reproductive Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frph.2026.1773150\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T06:20:48Z","timestamp":1779949248000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frph.2026.1773150\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":18,"alternative-id":["10.3389\/frph.2026.1773150"],"URL":"https:\/\/doi.org\/10.3389\/frph.2026.1773150","relation":{},"ISSN":["2673-3153"],"issn-type":[{"value":"2673-3153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,28]]},"article-number":"1773150"}}