Modeling and survival exploration of breast carcinoma: A statistical, maximum likelihood estimation, and artificial neural network perspective
Yükleniyor...
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The 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.
Açıklama
Anahtar Kelimeler
Risk Function, Reliability function, Multi-layer perceptrons, Artificial neural network, Maximum likelihood estimation
Kaynak
Artificial Intelligence in the Life Sciences
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
4
