Application of Bayesian Classifier for the Diagnosis of Dental Pain
| Autor: | Rima M. Davis, Rajendra U Acharya, Daphne D. Menezes, Gautam Singh, Toshio Tamura, Subhagata Chattopadhyay |
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| Rok vydání: | 2010 |
| Předmět: |
Computer science
Medicine (miscellaneous) Conditional probability table Health Informatics Machine learning computer.software_genre Bayes' theorem Naive Bayes classifier Health Information Management Cronbach's alpha Surveys and Questionnaires Linear regression Statistics Toothache medicine Humans Diagnostic Techniques and Procedures business.industry Bayes Theorem Artificial intelligence medicine.symptom business computer Hill climbing Classifier (UML) Algorithms Information Systems |
| Zdroj: | Journal of Medical Systems. 36:1425-1439 |
| ISSN: | 1573-689X 0148-5598 |
| DOI: | 10.1007/s10916-010-9604-y |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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