Bayesian variable selection in the AFT model with an application to the SEER breast cancer data
نویسندگان
چکیده
Zhen Zhang, Samiran Sinha2,∗, Tapabrata Maiti, and Eva Shipp 1 Department of Statistics, University of Chicago, Chicago, Illinois 2 Department of Statistics, Texas A&M University, College Station, Texas 3 Department of Statistics and Probability, Michigan State University, East Lansing, Michigan 4 Texas A&M Health Science Center, School of Rural Public Health, College Station, Texas ∗ email: [email protected]
منابع مشابه
Extracting Predictor Variables to Construct Breast Cancer Survivability Model with Class Imbalance Problem
Application of data mining methods as a decision support system has a great benefit to predict survival of new patients. It also has a great potential for health researchers to investigate the relationship between risk factors and cancer survival. But due to the imbalanced nature of datasets associated with breast cancer survival, the accuracy of survival prognosis models is a challenging issue...
متن کاملApplication of the Weibull Accelerated Failure Time Model in the Determination of Disease-Free Survival Rate of Patients with Breast Cancer
Background and Purpose: The goal of this study is application of the proportional hazards model (PH) and accelerated failure time model (AFT), with consideration Weibull distribution, to determine the level of effectiveness of the factors affecting on the level of disease-free survival (DFS) of the patients with breast cancer. Materials and Methods: Based on the retrospective descriptive stu...
متن کاملA Probabilistic Bayesian Classifier Approach for Breast Cancer Diagnosis and Prognosis
Basically, medical diagnosis problems are the most effective component of treatment policies. Recently, significant advances have been formed in medical diagnosis fields using data mining techniques. Data mining or Knowledge Discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. In this paper, Bayesian Classifier is used as a Non-linear dat...
متن کاملA Probabilistic Bayesian Classifier Approach for Breast Cancer Diagnosis and Prognosis
Basically, medical diagnosis problems are the most effective component of treatment policies. Recently, significant advances have been formed in medical diagnosis fields using data mining techniques. Data mining or Knowledge Discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. In this paper, Bayesian Classifier is used as a Non-linear dat...
متن کاملDiagnosis of Breast Cancer Subtypes using the Selection of Effective Genes from Microarray Data
Introduction: Early diagnosis of breast cancer and the identification of effective genes are important issues in the treatment and survival of the patients. Gene expression data obtained using DNA microarray in combination with machine learning algorithms can provide new and intelligent methods for diagnosis of breast cancer. Methods: Data on the expression of 9216 genes from 84 patients across...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2015