An Enhanced Machine Learning Approach for Early Detection ‎of Malnutrition in Toddlers, Using Health and Social‎Determinants Data - Karunanithi, Dr.J.; Sundaravalli, E. (International Journal of Basic and Applied Sciences, 2025)
National progress and its global standing start with the health of the early-age population. The well-nourished children have become skilled, ‎constructive, and efficient resources to make innovative establishments. This will lead their country to be stronger and more successful in the ‎global economy and its overall well-being. Many countries like India, Japan, the US, Germany, South Korea, and Switzerland started to ‎invest in children’s health care and early education systems. The Indian government has implemented several schemes and plans to ‎prioritize child health care to be more competitive globally. Still, child malnutrition remains one of the challenging issues due to its vast ‎population and cultural differences. These challenges will be rectified by monitoring the child's nutrition status with powerful actions and ‎plans. The machine learning models were helpful in various sectors of decision-making mechanisms. This paper implemented the enhanced ‎machine learning model to detect the malnutrition status of children up to 5 years old. The malnutrition status can be measured by several ‎indicators. This model takes diet routine, socio-economic status, family type, health check-up routines, vaccination routines, exclusive ‎breastfeeding, demographic data such as gender, age, and anthropometric measures such as height and weight to estimate the nutrition status ‎of the toddlers. The enhanced machine learning model combines the results of the Support Vector Machine, Logistic regression, Gaussian ‎Naïve Bayes, and Random Forest machine learning models to produce an output with 94% accuracy, demonstrating the problem using real-world instances‎.
Keywords: Enhanced; Machine; Malnutrition; Data; Health