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

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 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
Author(s): K. Fasna (corresponding author) [1]; Saima Yunus Khan [1]; Ayesha Ahmad [2]; Manoj Kumar Sharma [3] Introduction Childhood is an essential stage in a child's life. Early childhood caries [...]