Prediction of Child Malnutrition Using AI/ML Algorithm

Synthetic Intelligence (AI) and machine learning (ML) are hastily transforming numerous fields, imparting advanced and faster outcomes. As facts volume grows, leveraging AI and ML becomes crucial for reading big datasets correctly. This paper surveys the software of ML techniques in predicting baby malnutrition, a crucial thing in allowing early interventions and prioritizing remedies. Key clinical factors which includes age, gender, height, and weight are utilized in building predictive fashions. Current research employs supervised getting to know algorithms like SVM, Linear Regression, selection bushes, AdaBoost, and Random wooded area, reaching accuracies of 85% to 95%. But, maximum models lack real-time applicability.