Anemia Prediction Using Machine Learning Algorithms

Anemia is one of the issues with global public health, mostly affecting children and expectant mothers. A WHO study states that 42% of children under the age of six and 40% of pregnant mothers worldwide are anemic. Iron deficiency is the cause of this condition that affects 33% of people worldwide. The detection of anemia in modern times is one method used in the non-invasive diagnosis or detection for clinical disorders such as the use of machine learning algorithms. This work used a machine learning approach to identify iron deficiency anemia by utilizing the algorithms Naive Bayes, Random Forests, SVM, Logistic Regression, and decision trees. The Logistic Regression approach yielded the highest accuracy in this investigation.