NDLI: Application of Bayesian Classifier for the Diagnosis of Dental Pain

Content Provider	Springer Nature Link
Author	Chattopadhyay, Subhagata Davis, Rima M. Menezes, Daphne D. Singh, Gautam Acharya, Rajendra U. Tamura, Toshio
Copyright Year	2010
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 141010 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.
Starting Page	1425
Ending Page	1439
Page Count	15
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	36
Issue Number	3
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2010-10-13
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian classifier Dental pain Hill climbing algorithm Learning rate Regressions Health Informatics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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