A Comparative Study of Bayes Net, Naive Bayes and Averaged One-Dependence Estimators for Osteoporosis Analysis.
This paper presents an evaluation of the accuracy of the Bayesian classifiers: Bayes Net, Naive Bayes and Averaged One-Dependence Estimator, to support diagnoses of osteopenia and osteoporosis. All classifiers showed good results, thus, given data, it is possible to produce a reasonably accurate estimate of the diagnosis.
Research Group in Information and Communications Technology in Health, UNESC, Criciúma, SC, Brazil.
Priscyla
P
Waleska Simões
Research Group in Information and Communications Technology in Health, UNESC, Criciúma, SC, Brazil.
Leandro Luiz
LL
Mazzuchello
Research Group in Information and Communications Technology in Health, UNESC, Criciúma, SC, Brazil.
Larissa Letieli
LL
Toniazzo de Abreu
Research Group in Information and Communications Technology in Health, UNESC, Criciúma, SC, Brazil.
Diego
D
Garcia
Research Group in Information and Communications Technology in Health, UNESC, Criciúma, SC, Brazil.
Maitê Gabriel
MG
dos Passos
Research Group in Information and Communications Technology in Health, UNESC, Criciúma, SC, Brazil.
Ramon
R
Venson
Postgraduate Program in Public Health, UNESC, Criciúma, SC, Brazil.
Luciane
L
Bisognin Ceretta
Research Group in Information and Communications Technology in Health, UNESC, Criciúma, SC, Brazil.
Ana Carolina
AC
Veiga Silva
Postgraduate Program in Public Health, UNESC, Criciúma, SC, Brazil.
Maria Inês
MI
da Rosa
Postgraduate Program in Health Technology, Pontifícia Universidade Católica do Paraná, Curitiba, PR, Brazil.
Paulo João
PJ
Martins
D000465:Algorithms; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D010024:Osteoporosis / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity