Exploring the association between early childhood caries, malnutrition, and anemia by machine learning algorithm

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%. Conclusion: 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.

Keywords

• ecc
• machine learning algorithms
• malnutrition