OBJECTIVES: This study aimed to use machine learning algorithms to build a prediction model of the first permanent molar caries of 9-year-old children in Suzhou and screen out risk factors. METHODS: Random stratified whole group sampling was applied to randomly select 9-year-old students from 38 primary schools in 14 townships and streets in Wuzhong District for oral examination and questionnaire survey. Multifactor Logistics regression was used to analyze the risk factors of tooth decay. The data set was randomly divided into training sets and verification sets according to 8∶2, and R 4.3.1 was used to build five machine learning algorithms: random forest, decision tree, extreme gradient boosting (XGBoost), Logistics regression, and lightweight gradient enhancement (LightGBM). The predictive effect of these five models was evaluated using the area under the characteristic curve (AUC). The marginal contribution of quantitative characteristics to the caries prediction model was determined through Shapley additive explanations (SHAP). RESULTS: This study included 7 225 samples that met the standard. The caries rate of the first permanent molar was 54.96%. Multifactor Logistic regression analysis showed that sweet drinks, dessert and candy, snack frequency, and snacks before going to bed after brushing teeth were correlated with the occurrence of first permanent molar caries (P<0.05). The AUC values of decision tree, Logistic regression, LightGBM, random forest, and XGBoost were 75.5%, 83.9%, 88.6%, 88.9%, and 90.1%, respectively. Compared with the variables after single heat coding, the SHAP value of high-frequency sweets (such as dessert candy ≥2 times a day, mother's sugary diet ≥2 times a day) and bad oral hygiene habits (such as frequent snacks before going to bed after brushing teeth and irregular brushing teeth) exhibited the highest positive. CONCLUSIONS XGBoost algorithm has a good prediction effect for first permanent molar caries in 9-year-old children. High-frequency sweet factors and bad oral hygiene habits have a strong positive impact on the risk of first permanent molar caries and are key drivers that can be used in the formulation of targeted interventions.
Humans ; Dental Caries/epidemiology* ; Child ; Machine Learning ; China/epidemiology* ; Molar ; Risk Factors ; Female ; Logistic Models ; Male ; Decision Trees ; Algorithms

Objective To investigate the effect of low dose low molecular weight heparin (LMWH) on acute pancreatitis (AP). Methods 98 AP patients who were admitted in our hospital from 2002 to 2008 were randomly divided into anticoagulant therapy group (n = 40) and control group (n = 58). Anticoagulant therapy group consisted of 15 cases of severe acute pancreatitis (SAP) and 25 cases of mild acute pancreatitis (MAP) ; while there were 19 cases of SAP and 39 cases of MAP in control group. The patients of control group received conventional treatment, and conventional therapy together with 3 000 U LMWH subcutaneous injection every 12 hours were used in anticoagulant therapy group for two weeks. The changes of APACHE II score, complication rate, mortality and length of hospital stay were observed and the coagulation changes before and after anticoagulant therapy were documented. Results 7 days later, the APACHE II score, complication rate, mortality and length of hospital stay of SAP patients in the anticoagulant therapy group were 9. 9 ±4. 9, 20% , 13.3% , (20.6 ±10.4)d, respectively; while they were 12. 2 ±4.8, 42. 1%, 47.4%, (28. 2 ± 12. 5) d, respectively, in the control group, and the difference was statistically significant (P < 0. 05). The corresponding values were not statistically significantly different among MAP patients in the two groups. The coagulation after treatment in anticoagulant therapy group was not statistically different with that before treatment. Conclusions Low dose LMWH could reduce the rate of complication rate, mortality and decrease the length of hospital stay, without complication of hemorrhage, which should be recommended in the early phase of SAP.