@article{ARA_LOUZADA_MILAN_2018, title={CLASSIFICATION BINARY MODELS FOR BIOMEDICAL DATA: SIMPLE PROBABILISTIC NETWORKS AND LOGISTIC REGRESSION}, volume={36}, url={https://biometria.ufla.br/index.php/BBJ/article/view/114}, DOI={10.28951/rbb.v36i1.114}, abstractNote={<p>In the biomedical area a critical factor is whether a classication model is&nbsp;accurate enough in order to provide correct classication whether or not a patient has a&nbsp;certain disease. Several techniques may be used in order to accommodate such situation.&nbsp;In this context, Bayesian networks have emerged as a practical classication technology&nbsp;with successful applications in many elds. At the same time, logistic regression is&nbsp;a widely used statistical classication method and evidenced in the literature. In the&nbsp;current paper we focus on investigating the preditive performance of a probabilistic&nbsp;networks in its simple particular case, the so called naive Bayes network, compared to&nbsp;the logistic regression. A systematic simulation study is performed and the procedures&nbsp;are illustrated in some benchmark biomedical data sets.&nbsp;data sets.</p>}, number={1}, journal={Brazilian Journal of Biometrics}, author={ARA, Anderson and LOUZADA, Francisco and MILAN, Luis Aparecido}, year={2018}, month={Mar.}, pages={48–55} }