Data Mining and Visualization for Toddler Nutrition Monitoring in Community Health Centers

This research employed data mining methodologies to evaluate the nutritional well-being of children aged under 5 by utilizing standard anthropometric indices.Employing a knowledge discovery approach, data sourced from an Indonesian community health center was analyzed.The study focused on four key anthropometric indices-weight/age, height/age, weight/height, and BMI/age-as defining attributes.Through the application of k-means clustering, the toddlers were segmented into distinct groups exhibiting similar nutritional traits.The analysis of these clusters delineated three categories, highlighting groups with satisfactory nutritional indicators, those with low weight/height ratios, and others displaying inadequate height/age metrics.While a significant proportion of toddlers showcased acceptable nutritional profiles across these clusters, a substantial portion demonstrated irregular height or weight metrics.The utilization of Tableau visualizations yielded actionable insights, showcasing the practical utility of data mining in assessing nutritional status, potentially guiding health center interventions.However, to consolidate these findings, future research endeavors with larger sample sizes are imperative.