Assessment of Bayesian Estimators for Osteoporosis Analysis


  • Leandro Luiz Mazzuchello Universidade do Extremo Sul Catarinense (UNESC)
  • Larissa Letieli Toniazzo de Abreu Universidade do Extremo Sul Catarinense (UNESC)
  • Carolina Pedrassani de Lira Universidade do Extremo Sul Catarinense (UNESC)
  • Maitê Gabriel dos Passos Universidade do Extremo Sul Catarinense (UNESC)
  • Ramon Venson Universidade do Extremo Sul Catarinense (UNESC)
  • Abigail Lopes Universidade do Extremo Sul Catarinense (UNESC)
  • Diego Garcia Universidade do Extremo Sul Catarinense (UNESC)
  • Maria Marlene Souza Pires Universidade Federal de Santa Catarina (UFSC)
  • Eros Comunello Universidade Federal de Santa Catarina (UFSC)
  • Luciane Bisognin Ceretta Universidade do Extremo Sul Catarinense (UNESC)
  • Paulo João Martins Universidade do Extremo Sul Catarinense (UNESC)
  • Priscyla Waleska Simões Universidade do Extremo Sul Catarinense (UNESC)



medical informatics, artificial intelligence, data mining, bayes theorem, osteoporosis


Background: Bayesian classifiers have the advantage of determining the class to which a given record belongs compared to traditional classifiers, taking as base the probability of an element belonging to a class. Thus, the diagnosis of diseases such as osteoporosis and osteopenia can become faster and precise.This paper presents an assessment of the accuracy of the Bayesian classifiers Bayes Net, Naive Bayes and Averaged One-Dependence Estimators to support diagnoses of osteopenia and osteoporosis. Method: The methodology that guided the development of this research relied on the choice of database, the study of the Bayes Net, Naive Bayes and Averaged One-Dependence Estimators algorithms, and the description of the experiments. Results: The algorithm with the highest specificity was Bayes Net, (53.0±0.27). The highest accuracy was obtained using the AODE classifier (83.0±0.17). Our results showed higher mean instances correctly classified using the Naive Bayes algorithm (82.84±14.42), and the average of incorrectly classified instances was higher for Bayes Net (17.46±14.76). Conclusion: Based on the statistical measures analyzed in the experiments (instances classified correctly and incorrectly, the kappa coefficient, mean absolute error, sensitivity, specificity, accuracy, recall, F-measure, and Area Under Curve (AUC)), all classifiers showed good results, thus, given these data, it is possible to produce a reasonably accurate estimate of the diagnosis.


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