Multi-label Classification of Anemia Patients

This work examines the application of machine learning to an important area of medicine which aims to diagnose paediatric patients with beta-thalassemia minor, iron deficiency anemia or the co-occurrence of these ailments. Iron deficiency anemia is a major cause of microcytic anemia and is considered an important task in global health. Whilst existing methods, based on linear equations, are proficient at distinguishing between the two classes of anemia, they fail to identify the co-occurrence of this issues. Machine learning algorithms, however, can induce non-linear decision boundaries that enable accurate classification within complex domains. Through a multi-label classification technique, known as problem transformations, we convert the learning task to one that is appropriate for machine learning and examine the effectiveness of machine learning algorithms on this domain. Our results show that machine learning classifiers produce good overall accuracy and are able to identify instances of the co-occurrence class unlike the existing methods.