Performance Evaluation of various ML Algorithms for PCOS Diagnosis

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dc.contributor.authorSonam Juneja
dc.contributor.authorPannee Suanpang
dc.contributor.authorManoj Gupta
dc.contributor.authorNitesh Kumar Bhati
dc.contributor.authorBhoopesh Singh Bhati
dc.contributor.authorChanyanan Somthawinpongsai
dc.contributor.authorAziz Nanthaamornphong
dc.contributor.correspondenceS. Juneja; Department of CSE, Chandigarh University, Gharuan, India; email: sonam.december@gmail.com
dc.date.accessioned2025-03-10T07:34:20Z
dc.date.available2025-03-10T07:34:20Z
dc.date.issued2024
dc.description.abstractPCOS is a common endocrine disturbance leading to anovulation and subsequent severe health disorders such as cardiovascular events, type 2 diabetes, and infertility. An early and correct diagnosis is crucial to managing the disease and optimizing clinical outcomes. Traditional methods used to diagnose PCOS involve physical examinations and hormone testing but are not always conclusive, particularly at an initial stage. One technology that could substitute exploring large datasets and identifying magnified patterns that might assist in predict disease is machine learning. In this paper, we examine whether numerous ML algorithms can recommend the possibility for women to have PCOS. We use a dataset from a Google Collaboratory in which our features differ from the traditional diagnostic criteria for PCOS. This is the distinctive part of the study that enables us to test the possible advantages and disadvantages of including this extra information in the forecasting models. We will test the efficacy of such characteristics in determining the precise PCOS patients across a variety of classification models. The result of the current research will contribute to the growing body of evidence suggesting that machine learning may be used to identify diseases sooner, and promote women better manage their health. © 2024 IEEE.
dc.identifier.citationIEMECON 2024 - 12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks
dc.identifier.doi10.1109/IEMECON62401.2024.10846014
dc.identifier.isbn979-835038731-5
dc.identifier.scopus2-s2.0-85218117424
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4471
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights.holderScopus
dc.subjectArtificial intelligence (AI)
dc.subjectMachine Learning (ML)
dc.subjectMedical Disease
dc.subjectPCOD
dc.subjectPerformance
dc.titlePerformance Evaluation of various ML Algorithms for PCOS Diagnosis
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85218117424&doi=10.1109%2fIEMECON62401.2024.10846014&partnerID=40&md5=f43ef303e5c6c5090fade49c093c2d79
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