Application of bayesian classifier for the diagnosis of dental pain

Subhagata Chattopadhyay, Rima M. Davis, Daphne D. Menezes, Gautam Singh, Rajendra U. Acharya, Toshio Tamura

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

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 naïve 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.

Original languageEnglish
Pages (from-to)1425-1439
Number of pages15
JournalJournal of Medical Systems
Volume36
Issue number3
DOIs
Publication statusPublished - 01-06-2012
Externally publishedYes

Fingerprint

Toothache
Dentists
Tooth
Classifiers
Oral Pathology
Stomatognathic Diseases
Pain
Linear Models
Analysis of Variance
Pathology
Analysis of variance (ANOVA)
Linear regression
Surveys and Questionnaires
Testing

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management

Cite this

Chattopadhyay, S., Davis, R. M., Menezes, D. D., Singh, G., Acharya, R. U., & Tamura, T. (2012). Application of bayesian classifier for the diagnosis of dental pain. Journal of Medical Systems, 36(3), 1425-1439. https://doi.org/10.1007/s10916-010-9604-y
Chattopadhyay, Subhagata ; Davis, Rima M. ; Menezes, Daphne D. ; Singh, Gautam ; Acharya, Rajendra U. ; Tamura, Toshio. / Application of bayesian classifier for the diagnosis of dental pain. In: Journal of Medical Systems. 2012 ; Vol. 36, No. 3. pp. 1425-1439.
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Chattopadhyay, S, Davis, RM, Menezes, DD, Singh, G, Acharya, RU & Tamura, T 2012, 'Application of bayesian classifier for the diagnosis of dental pain', Journal of Medical Systems, vol. 36, no. 3, pp. 1425-1439. https://doi.org/10.1007/s10916-010-9604-y

Application of bayesian classifier for the diagnosis of dental pain. / Chattopadhyay, Subhagata; Davis, Rima M.; Menezes, Daphne D.; Singh, Gautam; Acharya, Rajendra U.; Tamura, Toshio.

In: Journal of Medical Systems, Vol. 36, No. 3, 01.06.2012, p. 1425-1439.

Research output: Contribution to journalArticle

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Chattopadhyay S, Davis RM, Menezes DD, Singh G, Acharya RU, Tamura T. Application of bayesian classifier for the diagnosis of dental pain. Journal of Medical Systems. 2012 Jun 1;36(3):1425-1439. https://doi.org/10.1007/s10916-010-9604-y