Application of Bayesian Classifier for the Diagnosis of Dental Pain

  • Authors:

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

  • Affiliations:

  • School of Computer Studies, Department of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, India 761008;Department of Biomedical Engineering, Manipal Institute of Technology, Manipal University, Manipal, India 576 104;Department of Biomedical Engineering, Manipal Institute of Technology, Manipal University, Manipal, India 576 104;Department of Biomedical Engineering, Manipal Institute of Technology, Manipal University, Manipal, India 576 104;Department of Electronic and Communication Engineering, Ngee Ann Polytechnic, Clementi, Singapore 599489;Department of Medical System Engineering, Chiba University, Chiba, Japan 263-8522

  • Venue:
  • Journal of Medical Systems
  • Year:
  • 2012

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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驴=驴10) using a set of 14 pain parameters (P驴=驴14). A questionnaire is developed using these variables and filled up by ten dentists (n驴=驴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.