J Med Syst - Application of Bayesian classifier for the diagnosis of dental pain.

Tópicos

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Resumo

Toothache is the most common symptom encountered in dental practice. It is subjective and hence, there is a possibility of under or over diagnosis of oral pathologies where patients present with only toothache. Addressing the issue, the paper proposes a methodology to develop a Bayesian classifier for diagnosing some common dental diseases (D=10) using a set of 14 pain parameters (P=14). A questionnaire is developed using these variables and filled up by ten dentists (n=10) with various levels of expertise. Each questionnaire is consisted of 40 real-world cases. Total 14*10*10 combinations of data are hence collected. The reliability of the data (P and D sets) has been tested by measuring (Cronbach's alpha). One-way ANOVA has been used to note the intra and intergroup mean differences. Multiple linear regressions are used for extracting the significant predictors among P and D sets as well as finding the goodness of the model fit. A na?ve Bayesian classifier (NBC) is then designed initially that predicts either presence/absence of diseases given a set of pain parameters. The most informative and highest quality datasheet is used for training of NBC and the remaining sheets are used for testing the performance of the classifier. Hill climbing algorithm is used to design a Learned Bayes' classifier (LBC), which learns the conditional probability table (CPT) entries optimally. The developed LBC showed an average accuracy of 72%, which is clinically encouraging to the dentists.

Resumo Limpo

toothach common symptom encount dental practic subject henc possibl diagnosi oral patholog patient present toothach address issu paper propos methodolog develop bayesian classifi diagnos common dental diseas d use set pain paramet p questionnair develop use variabl fill ten dentist n various level expertis questionnair consist realworld case total combin data henc collect reliabl data p d set test measur cronbach alpha oneway anova use note intra intergroup mean differ multipl linear regress use extract signific predictor among p d set well find good model fit nave bayesian classifi nbc design initi predict either presenceabs diseas given set pain paramet inform highest qualiti datasheet use train nbc remain sheet use test perform classifi hill climb algorithm use design learn bay classifi lbc learn condit probabl tabl cpt entri optim develop lbc show averag accuraci clinic encourag dentist

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