Modeling and survival exploration of breast carcinoma: A statistical, maximum likelihood estimation, and artificial neural network perspective

Basit Öğe Kaydı
dc.contributor.authorShafiq, Anum
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorSindhu, Tabassum Naz
dc.contributor.authorLone, Showkat Ahmad
dc.contributor.authorAbushal, Tahani A.
dc.date.accessioned2023-11-13T08:39:40Z
dc.date.available2023-11-13T08:39:40Z
dc.date.issued2023en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractThe core objective of this research is to describe the behavior of the distribution using the MLE method to es timate its parameters, as well as to determine the optimal Artificial Neural Network method by comparing it to the maximum likelihood estimation method and applying it to real data for breast cancer patients to determine survival, risk, and other survival study functions of the log-logistic distribution. The parameters were defined in the input layer of the artificial neural network developed for the purpose of survival analysis and reliability function, hazard rate function, probability density function, reserved hazard rate function, Mills ratio, Odd function and CHR values were obtained in the output layer. The findings show that risk function increases with the increase in the time of infection and then decreases for a group of breast cancer patients under study, which corresponds to the theoretical properties of this according to the practical conclusions. The examination of survival analysis reveals that practical conclusions correspond to the theoretical properties of log-logistic dis tribution. Artificial neural networks have proven to be one of the ideal tools that can be used to predict various vital parameters, especially survival of cancer patients, with their high predictive capabilities.en_US
dc.identifier.doi10.1016/j.ailsci.2023.100082en_US
dc.identifier.scopus2-s2.0-85171393546en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/7006
dc.identifier.urihttps://doi.org/10.1016/j.ailsci.2023.100082
dc.identifier.volume4en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofArtificial Intelligence in the Life Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRisk Function, Reliability function, Multi-layer perceptrons, Artificial neural network, Maximum likelihood estimationen_US
dc.titleModeling and survival exploration of breast carcinoma: A statistical, maximum likelihood estimation, and artificial neural network perspectiveen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
1-s2.0-S2667318523000260-main.pdf
Boyut:
17.37 MB
Biçim:
Adobe Portable Document Format
Açıklama:
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.56 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu