Abstract：
OBJECTIVE: The objective of this study was to determine the prevalence of early childhood caries in children with severe acute malnutrition (SAM) and also the hierarchy of association if any with malnutrition, anemia, and other risk factors with ECC using machine learning algorithms.
METHODS: A hospital-based preventive and interventional study was conducted on SAM children (age = 2 to <6 years) who were admitted to the malnutrition treatment unit (MTU). An oral examination for early childhood caries status was done using the deft index. The anthropometric measurements and blood examination reports were recorded. Oral health education and preventive dental treatments were given to the admitted children. Three machine learning algorithms (Random Tree, CART, and Neural Network) were applied to assess the relationship between early childhood caries, malnutrition, anemia, and the risk factors.
RESULTS: The Random Tree model showed that age was the most significant factor in predicting ECC with predictor importance of 98.75%, followed by maternal education (29.20%), hemoglobin level (16.67%), frequency of snack intake (9.17%), deft score (8.75%), consumption of snacks (7.1%), breastfeeding (6.25%), severe acute malnutrition (5.42%), frequency of sugar intake (3.75%), and religion at the minimum predictor importance of 2.08%.
CONCLUSIONS: Anemia and malnutrition play a significant role in the prediction, hence in the causation of ECC. Pediatricians should also keep in mind that anemia and malnutrition have a negative impact on children\'s dental health. Hence, Pediatricians and Pediatric dentist should work together in treating this health problem.
METHODS: A hospital-based preventive and interventional study was conducted on SAM children (age = 2 to <6 years) who were admitted to the malnutrition treatment unit (MTU). An oral examination for early childhood caries status was done using the deft index. The anthropometric measurements and blood examination reports were recorded. Oral health education and preventive dental treatments were given to the admitted children. Three machine learning algorithms (Random Tree, CART, and Neural Network) were applied to assess the relationship between early childhood caries, malnutrition, anemia, and the risk factors.
RESULTS: The Random Tree model showed that age was the most significant factor in predicting ECC with predictor importance of 98.75%, followed by maternal education (29.20%), hemoglobin level (16.67%), frequency of snack intake (9.17%), deft score (8.75%), consumption of snacks (7.1%), breastfeeding (6.25%), severe acute malnutrition (5.42%), frequency of sugar intake (3.75%), and religion at the minimum predictor importance of 2.08%.
CONCLUSIONS: Anemia and malnutrition play a significant role in the prediction, hence in the causation of ECC. Pediatricians should also keep in mind that anemia and malnutrition have a negative impact on children\'s dental health. Hence, Pediatricians and Pediatric dentist should work together in treating this health problem.
摘要：
目的：本研究的目的是确定严重急性营养不良（SAM）儿童早期龋齿的患病率，以及与营养不良的关联等级。贫血,以及其他使用机器学习算法的ECC风险因素。
方法：对入住营养不良治疗单元（MTU）的SAM儿童（年龄=2至<6岁）进行了一项基于医院的预防和干预研究。使用灵巧指数进行了儿童早期龋齿状况的口腔检查。记录人体测量和血液检查报告。对入院儿童进行口腔健康教育和预防性牙科治疗。三种机器学习算法(随机树，CART,和神经网络)用于评估儿童早期龋齿之间的关系，营养不良,贫血,和风险因素。
结果：随机树模型表明，年龄是预测ECC的最重要因素，预测重要性为98.75％，其次是产妇教育(29.20%)，血红蛋白水平（16.67%），零食摄入频率(9.17%)，灵巧得分(8.75%)，零食消费(7.1%)，母乳喂养(6.25%),严重急性营养不良（5.42%），糖摄入频率（3.75%），和宗教的最低预测重要性为2.08%。
结论：贫血和营养不良在预测中起重要作用，因此在ECC的因果关系中。儿科医生还应该记住，贫血和营养不良对儿童的牙齿健康有负面影响。因此，儿科医生和儿科牙医应共同努力治疗这一健康问题。
方法：对入住营养不良治疗单元（MTU）的SAM儿童（年龄=2至<6岁）进行了一项基于医院的预防和干预研究。使用灵巧指数进行了儿童早期龋齿状况的口腔检查。记录人体测量和血液检查报告。对入院儿童进行口腔健康教育和预防性牙科治疗。三种机器学习算法(随机树，CART,和神经网络)用于评估儿童早期龋齿之间的关系，营养不良,贫血,和风险因素。
结果：随机树模型表明，年龄是预测ECC的最重要因素，预测重要性为98.75％，其次是产妇教育(29.20%)，血红蛋白水平（16.67%），零食摄入频率(9.17%)，灵巧得分(8.75%)，零食消费(7.1%)，母乳喂养(6.25%),严重急性营养不良（5.42%），糖摄入频率（3.75%），和宗教的最低预测重要性为2.08%。
结论：贫血和营养不良在预测中起重要作用，因此在ECC的因果关系中。儿科医生还应该记住，贫血和营养不良对儿童的牙齿健康有负面影响。因此，儿科医生和儿科牙医应共同努力治疗这一健康问题。
