MCMC impute missing values and Bayesian variable selection for logistic regression model to predict Pima Indian Diabetes

Autor:	Gongli Li, Yueze Liu, Han Li, Ruikuan Yao, Chenyang Li
Rok vydání:	2021
Předmět:	History Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1865:042087
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1865/4/042087
Popis:	Diabetes mellitus is a metabolic disease that causes high blood sugar. The risk factor of diabetes can be reduced significantly by early precise prediction. Lots of literatures published for diabetes prediction, but nearly all of them proposed frequentist Machine learning algorithm to classify and build models. Besides, their data pre-processing methods are not professional. In this literature, we are proposing MCMC filling missing values and Bayesian variable selection for logistic regression model to classify diabetes patients. It shows great performance (AUC = 0.884, sensitivity = 0.805, specificity = 0.875).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3f2a001c07c8415fb80699eed0902917 https://doi.org/10.1088/1742-6596/1865/4/042087 Zobrazit plný text záznamu