SPOSDS: A smart polycystic ovary syndrome diagnostic system using machine learning

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dc.authorid0000-0002-5987-7101en_US
dc.authorid0000-0003-1688-8772en_US
dc.authorid0000-0001-5155-022Xen_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorTiwari, Shamik
dc.contributor.authorKane, Lalit
dc.contributor.authorKoundal, Deepika
dc.contributor.authorJain, Anurag
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-10-26T13:39:54Z
dc.date.available2023-10-26T13:39:54Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis work has been supported by Taif University Researchers Sup-porting Project Number (TURSP-2020/114) , Taif University, Taif, Saudi Arabia.en_US
dc.description.abstractPolycystic Ovary Syndrome (PCOS) is a hormonal disorder that affects a large percentage of women of reproductive age. PCOS causes imbalanced or delayed menstrual cycles and produces high levels of the male hormone. The ovaries may create a significant number of little fluid-filled sacs (follicles) yet fail to discharge eggs regularly. The actual cause of PCOS is uncertain. However, early exposure and curing, as well as weight loss, may lower the threat of long-term complications. This study focuses on PCOS diagnosis based on a clinical dataset supplied by Kottarathil, accessible via its Kaggle repository. Non-invasive screening parameters are used to evaluate a range of machine learning approaches for screening PCOS patients without the use of invasive diagnostics. According to the findings of the experiments, the Random Forest (RF) method outperforms the other prominent machine learning algorithms with an accuracy of 93.25%. Further, the out-of-bag (OOB) error is utilized for assessing the prediction performance of RF.en_US
dc.description.sponsorshipTaif University Researchers Supporting Project, Taif University, Taif, Saudi Arabia [TURSP-2020/114]; Taif University, Taif, Saudi Arabiaen_US
dc.identifier.citationTiwari, S., Kane, L., Koundal, D., Jain, A., Alhudhaif, A., Polat, K., ... & Althubiti, S. A. (2022). SPOSDS: A smart Polycystic Ovary Syndrome diagnostic system using machine learning. Expert Systems with Applications, 203, 117592.en_US
dc.identifier.doi10.1016/j.eswa.2022.117592
dc.identifier.endpage14en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85130160464en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2022.117592
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11796
dc.identifier.volume203en_US
dc.identifier.wosWOS:000803735500008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPolycystic Ovary Syndromeen_US
dc.subjectSmart Diagnosisen_US
dc.subjectMachine Learningen_US
dc.subjectRandom Foresten_US
dc.subjectOut of Bag Erroren_US
dc.subjectToolen_US
dc.titleSPOSDS: A smart polycystic ovary syndrome diagnostic system using machine learningen_US
dc.typeArticleen_US

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