J Biomed Inform - Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).

Tópicos

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Resumo

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson's patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson's disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

Resumo Limpo

multidimension bayesian network classifi mbcs probabilist graphic model recent propos deal multidimension classif problem instanc data set assign one class variabl paper propos markov blanketbas approach learn mbcs data basic consist determin markov blanket around class variabl use hiton algorithm specifi direct mbc subgraph approach appli predict problem european qualiti life dimens eqd item parkinson diseas questionnair pdq order estim healthrel qualiti life parkinson patient fivefold crossvalid experi carri random generat synthet data set yeast data set well realworld parkinson diseas data set contain patient experiment studi includ comparison addit bayesian networkbas approach back propag multilabel learn multilabel knearest neighbor multinomi logist regress ordinari least squar censor least absolut deviat show encourag result term predict accuraci well identif depend relationship among class featur variabl

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