USING MACHINE LEARNING ALGORITHMS TO PREDICT ANEMIA IN CHILDREN UNDER 5 YEARS: A COMPARATIVE STUDY
Keywords:
Anemia, Children, Machine Learning, Random Forest, Ensemble Learning, Feature AnalysisAbstract
Anemia is a common health issue in young children, which can lead to serious consequences if not
detected and treated early. In this study, we explore the potential of using machine learning algorithms to
predict anemia in children under the age of five. We collected data from Kanti Children Hospital in Nepal,
consisting of 700 data records, and selected six different machine learning algorithms for verification and
validation, including Random Forest, Decision Tree, Naïve Bayes, Artificial Neural Network, Support Vector
Machine, and Logistic Regression. The data was preprocessed, normalized, and balanced, and the algorithms
were applied to improve accuracy in predicting anemia. We also applied ensemble learning methods, including
Voting, Stacking, Bagging, and Boosting, to further improve performance. Our study found that Random
Forest was the best performer with an accuracy of 98.4%. Feature analysis indicated that selecting the best
features also contributed to improving accuracy. Balanced data was used to further validate the results. Our
study highlights the potential of machine learning in predicting and preventing diseases in the field of health
informatics, particularly in the case of anemia in young children.
