Department of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, Orissa, India.
J Med Syst. 2012 Jun;36(3):1425-39. doi: 10.1007/s10916-010-9604-y. Epub 2010 Oct 13.
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.
牙痛是牙科实践中最常见的症状。它是主观的，因此，在患者仅表现出牙痛的情况下，可能存在口腔病理学的漏诊或误诊。为了解决这个问题，本文提出了一种使用 14 个疼痛参数（P=14）来开发贝叶斯分类器诊断一些常见牙科疾病（D=10）的方法。使用这些变量开发了一份问卷，并由十位具有不同专业水平的牙医（n=10）填写。每个问卷都包含 40 个真实案例。因此，共收集了 141010 种组合的数据。通过测量（Cronbach 的 alpha）来测试数据（P 和 D 集）的可靠性。使用单因素方差分析来记录组内和组间的平均差异。使用多元线性回归从 P 和 D 集中提取显著预测因子，并找到模型拟合的优度。然后设计了一个朴素贝叶斯分类器（NBC），该分类器根据一组疼痛参数预测疾病的存在与否。使用最具信息量和最高质量的数据表来训练 NBC，其余数据表用于测试分类器的性能。使用爬山算法设计了一个学习贝叶斯分类器（LBC），该分类器可以最佳地学习条件概率表（CPT）条目。开发的 LBC 显示出平均 72%的准确率，这对牙医来说是具有临床意义的鼓励。
