A Decision Making System for the Treatment of Dental Caries

Mago, Vijay; Prasad, Bhanu; Bhatia, Ajay; Mago, Anjali

doi:10.1007/978-3-540-79005-1_12

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Mago et al [28] designed a system based on the Bayesian net to decide possible treatment plans for dental caries. Its operational effectiveness was attributed to the use of the Bayesian net.…”

Section: Current State Of Artmentioning

confidence: 99%

Application of Bayesian Classifier for the Diagnosis of Dental Pain

et al. 2010

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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.

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Section: Current State Of Artmentioning

confidence: 99%

Application of Bayesian Classifier for the Diagnosis of Dental Pain

et al. 2010

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“…In fact, the use of such systems can lead to a decrease in fear (due to lack of knowledge, skills, being alone) and increased self-esteem, especially in young physicians. The development and evolution of these systems can provide the satisfaction of the stakeholders of health care systems and it is also possible that by design and using decision-making systems in physician assistant’s portable devices or medical computers in medical offices providing real-time medical tools to clinical practitioners to make more reliable diagnoses [ 19 , 20 ].…”

Section: Main Textmentioning

confidence: 99%

A decision support system based on support vector machine for diagnosis of periodontal disease

2020

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Objective: Early diagnosis of many diseases is essential for their treatment. Furthermore, the existence of abundant and unknown variables makes more complicated decision making. For this reason, the diagnosis and classification of diseases using machine learning algorithms have attracted a lot of attention. Therefore, this study aimed to design a support vector machine (SVM) based decision-making support system to diagnosis various periodontal diseases. Data were collected from 300 patients referring to Periodontics department of Hamadan University of Medical Sciences, west of Iran. Among these patients, 160 were Gingivitis, 60 were localized periodontitis and 80 were generalized periodontitis. In the designed classification model, 11 variables such as age, sex, smoking, gingival index, plaque index and so on used as input and output variable show the individual's status as a periodontal disease. Results: Using different kernel functions in the design of the SVM classification model showed that the radial kernel function with an overall correct classification accuracy of 88.7% and the overall hypervolume under the manifold (HUM) value was to 0.912 has the best performance. The results of the present study show that the designed classification model has an acceptable performance in predicting periodontitis.

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“…FL based approaches have been developed and broadly used in medicine [13][14][15][16][17][18][19][20] and other fields [21,22]. In this research, we suggest the use of a FL system to provide a new approach for dental decision-making, one which aims to assist the dentist when choosing treatment plan(s) for a cracked or broken tooth.…”

Section: Introductionmentioning

confidence: 99%