PDF(Pubmed)
Abstract：
BACKGROUND: The aim of this study was to analyse the risk factors that affect oral health in adults and to evaluate the success of different machine learning algorithms in predicting these risk factors.
METHODS: This study included 2000 patients aged 18 years and older who were admitted to the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Gaziantep University, between September and December 2023. In this study, patients completed a 30-item questionnaire designed to assess the factors that affect the decayed, missing, and filled teeth (DMFT). Clinical and radiological examinations were performed, and DMFT scores were calculated after completion of the questionnaire. The obtained data were randomly divided into a 75% training group and a 25% test group. The preprocessed dataset was analysed using various machine learning algorithms, including naive Bayes, logistic regression, support vector machine, decision tree, random forest and Multilayer Perceptron algorithms. Pearson\'s correlation test was also conducted to assess the correlation between participants\' DMFT scores and oral health risk factors. The performance of each algorithm was evaluated to determine the most appropriate algorithm, and model performance was assessed using accuracy, precision, recall and F1 score on the test dataset.
RESULTS: A statistically significant difference was found between various factors and DMFT-based risk groups (p < 0.05), including age, sex, body mass index, tooth brushing frequency, socioeconomic status, employment status, education level, marital status, hypertension, diabetes status, renal disease status, consumption of sugary snacks, dry mouth status and screen time. When considering machine learning algorithms for risk group assessments, the Multilayer Perceptron model demonstrated the highest level of success, achieving an accuracy of 95.8%, an F1-score of 96%, and precision and recall rates of 96%.
CONCLUSIONS: Caries risk assessment using a simple questionnaire can identify individuals at risk of dental caries, determine the key risk factors, provide information to help reduce the risk of dental caries over time and ensure follow-up. In addition, it is extremely important to apply effective preventive treatments and to prevent the general health problems that are caused by the deterioration of oral health. The results of this study show the potential of machine learning algorithms for predicting caries risk groups, and these algorithms are promising for future studies.
METHODS: This study included 2000 patients aged 18 years and older who were admitted to the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Gaziantep University, between September and December 2023. In this study, patients completed a 30-item questionnaire designed to assess the factors that affect the decayed, missing, and filled teeth (DMFT). Clinical and radiological examinations were performed, and DMFT scores were calculated after completion of the questionnaire. The obtained data were randomly divided into a 75% training group and a 25% test group. The preprocessed dataset was analysed using various machine learning algorithms, including naive Bayes, logistic regression, support vector machine, decision tree, random forest and Multilayer Perceptron algorithms. Pearson\'s correlation test was also conducted to assess the correlation between participants\' DMFT scores and oral health risk factors. The performance of each algorithm was evaluated to determine the most appropriate algorithm, and model performance was assessed using accuracy, precision, recall and F1 score on the test dataset.
RESULTS: A statistically significant difference was found between various factors and DMFT-based risk groups (p < 0.05), including age, sex, body mass index, tooth brushing frequency, socioeconomic status, employment status, education level, marital status, hypertension, diabetes status, renal disease status, consumption of sugary snacks, dry mouth status and screen time. When considering machine learning algorithms for risk group assessments, the Multilayer Perceptron model demonstrated the highest level of success, achieving an accuracy of 95.8%, an F1-score of 96%, and precision and recall rates of 96%.
CONCLUSIONS: Caries risk assessment using a simple questionnaire can identify individuals at risk of dental caries, determine the key risk factors, provide information to help reduce the risk of dental caries over time and ensure follow-up. In addition, it is extremely important to apply effective preventive treatments and to prevent the general health problems that are caused by the deterioration of oral health. The results of this study show the potential of machine learning algorithms for predicting caries risk groups, and these algorithms are promising for future studies.
摘要：
背景：这项研究的目的是分析影响成人口腔健康的危险因素，并评估不同机器学习算法在预测这些危险因素方面的成功。
方法：本研究纳入2000名年龄在18岁及以上的口腔颌面放射科患者，牙科学院,加济安泰普大学,2023年9月至12月。在这项研究中,患者完成了一项30项问卷，旨在评估影响衰变的因素，失踪,和填充牙齿（DMFT）。进行了临床和放射学检查，完成问卷后计算DMFT评分。将获得的数据随机分为75％训练组和25％测试组。使用各种机器学习算法分析预处理的数据集，包括天真的贝叶斯,逻辑回归，支持向量机，决策树,随机森林和多层感知器算法。还进行了Pearson的相关性检验，以评估参与者的DMFT评分与口腔健康危险因素之间的相关性。评估每种算法的性能，以确定最合适的算法，并使用准确性评估模型性能，精度,测试数据集上的召回和F1分数。
结果：在各种因素和基于DMFT的风险组之间发现了统计学上的显着差异（p<0.05），包括年龄,性别,身体质量指数,刷牙频率,社会经济地位,就业状况,教育水平,婚姻状况,高血压,糖尿病状态，肾脏疾病状态，食用含糖零食，口干状态和屏幕时间。当考虑机器学习算法进行风险组评估时，多层感知器模型展示了最高水平的成功，达到95.8%的准确率，F1分数为96%，准确率和召回率为96%。
结论：使用简单的问卷进行龋齿风险评估可以识别有龋齿风险的个体，确定关键风险因素，提供信息，以帮助降低龋齿的风险随着时间的推移，并确保随访。此外,应用有效的预防性治疗和预防口腔健康恶化引起的一般健康问题极为重要。这项研究的结果显示了机器学习算法预测龋齿风险群体的潜力，这些算法对未来的研究很有希望。
方法：本研究纳入2000名年龄在18岁及以上的口腔颌面放射科患者，牙科学院,加济安泰普大学,2023年9月至12月。在这项研究中,患者完成了一项30项问卷，旨在评估影响衰变的因素，失踪,和填充牙齿（DMFT）。进行了临床和放射学检查，完成问卷后计算DMFT评分。将获得的数据随机分为75％训练组和25％测试组。使用各种机器学习算法分析预处理的数据集，包括天真的贝叶斯,逻辑回归，支持向量机，决策树,随机森林和多层感知器算法。还进行了Pearson的相关性检验，以评估参与者的DMFT评分与口腔健康危险因素之间的相关性。评估每种算法的性能，以确定最合适的算法，并使用准确性评估模型性能，精度,测试数据集上的召回和F1分数。
结果：在各种因素和基于DMFT的风险组之间发现了统计学上的显着差异（p<0.05），包括年龄,性别,身体质量指数,刷牙频率,社会经济地位,就业状况,教育水平,婚姻状况,高血压,糖尿病状态，肾脏疾病状态，食用含糖零食，口干状态和屏幕时间。当考虑机器学习算法进行风险组评估时，多层感知器模型展示了最高水平的成功，达到95.8%的准确率，F1分数为96%，准确率和召回率为96%。
结论：使用简单的问卷进行龋齿风险评估可以识别有龋齿风险的个体，确定关键风险因素，提供信息，以帮助降低龋齿的风险随着时间的推移，并确保随访。此外,应用有效的预防性治疗和预防口腔健康恶化引起的一般健康问题极为重要。这项研究的结果显示了机器学习算法预测龋齿风险群体的潜力，这些算法对未来的研究很有希望。
