Diagnosing cervical cancer using machine learning methods

Basit Öğe Kaydı
dc.authorid0000-0002-0101-897Xen_US
dc.contributor.authorCoşar Soğukkuyu, Derya Yeliz
dc.contributor.authorAta, Oğuz
dc.date.accessioned2022-08-08T12:59:43Z
dc.date.available2022-08-08T12:59:43Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractPre-diagnosis of any kind of cancer type has critical impact on person's life. According to World Health Organization Cervical cancer is still one of the most common gynecologic cancers in the world that affects women life. if medical experts focus on early diagnosis of the disease which is detecting symptomatic patients as early as possible, patients will have the best chance for successful treatment since Cervical Cancer is preventable. When cancer treatment is delayed, mortality rate decreases, and treatment becomes more complicated and expensive. Currently, systems based on Artificial Intelligence are used for decision making Machine learning techniques let automated detection of cervical cancer run more quickly and efficiently. In this study a novel ensemble approach is presented to predict the risk of cervical cancer by developing hybrid machine learning model. Multiple performance measurements such as Accuracy, precision score, recall score, and F1 are performed to evaluate the novel model. The results indicate that the proposed novel model can be effectively used to pre diagnosis of Cervical cancer with accuracy 97%.en_US
dc.identifier.citationSoğukkuyu, D. Y. C., Ata, O. (2022). Diagnosing cervical cancer using machine learning methods. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), IEEE.en_US
dc.identifier.isbn9781665468350
dc.identifier.scopus2-s2.0-85133969109
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2797
dc.indekslendigikaynakScopus
dc.institutionauthorCoşar Soğukkuyu, Derya Yeliz
dc.institutionauthorAta, Oğuz
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofHORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
dc.relation.isversionof10.1109/HORA55278.2022.9800033en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectCervical Canceren_US
dc.subjectClassificationen_US
dc.subjectDecision Systemsen_US
dc.subjectMachine Learningen_US
dc.titleDiagnosing cervical cancer using machine learning methods
dc.typeConference Object

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
Yazılım Mühendisliği Bölümü Koleksiyonu