文章基本信息

标题：CLASSIFICATION BINARY MODELS FOR BIOMEDICAL DATA: SIMPLE PROBABILISTIC NETWORKS AND LOGISTIC REGRESSION
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作者：Anderson ARA ; Francisco LOUZADA ; Luis Aparecido MILAN 等
期刊名称：Revista Brasileira de Biometria
印刷版ISSN：0102-0811
电子版ISSN：1983-0823
出版年度：2018
卷号：36
期号：1
页码：48-55
DOI：10.28951/rbb.v36i1.114
出版社：Universidade Federal de Lavras
摘要：In the biomedical area a critical factor is whether a classication model is accurate enough in order to provide correct classication whether or not a patient has a certain disease. Several techniques may be used in order to accommodate such situation.In this context, Bayesian networks have emerged as a practical classication technology with successful applications in many elds. At the same time, logistic regression is a widely used statistical classication method and evidenced in the literature. In the current paper we focus on investigating the preditive performance of a probabilistic networks in its simple particular case, the so called naive Bayes network, compared to the logistic regression. A systematic simulation study is performed and the procedures are illustrated in some benchmark biomedical data sets.data sets.
关键词：Binary classification; simple probabilistic networks; na¨ıve Bayes; logistic regression.