Classifying anemia types using artificial learning methods

[Display omitted] •Anemia is the most prevalent blood disease in the world.•As both disease and symptom of serious diseases anemia affects the quality of life.•Early and correct diagnosis is the first step towards treatment.•An original dataset of 12 anemia types is classified with state-of-the-art methods.•This system will enable the recognition of anemia in general practice conditions. The most common blood disease worldwide is anemia, defined by the World Health Organization as a condition in which the red blood cell count or oxygen-carrying capacity is insufficient. As both a disease and a symptom, this condition affects the quality of life. Early and correct diagnosis of the type of anemia is vital in terms of patient treatment. The increasing number of patients and hospital priorities, as well as difficulties in reaching medical specialists, may impede such a diagnosis. The present work proposes a system that will enable the recognition of anemia under general clinical practice conditions. For this system, a model constructed using four different artificial learning methods. Artificial Neural Networks, Support Vector Machines, Naïve Bayes, and Ensemble Decision Tree methods are used as classification algorithms. The models are evaluated with a dataset of 1663 samples and used 25 attributes, including hemogram data and general information such as age, sex, chronic diseases, and symptoms to diagnose 12 different anemia types. Data are collected by examining patient files at a university hospital in Turkey. In addition to all the data used by the doctors, the model also utilized eight different datasets created via particular feature selection techniques. The interface is designed to provide decision support to both medical consultants and medical students. Data are classified using the four different algorithms and an acceptable success ratio is obtained for each. Each model is validated using Classification Error, Area Under Curve, Precision, Recall, and F-score metrics in addition to Accuracy values. The highest accuracy (85.6%) achieved using Bagged Decision Trees, followed by Boosted Trees (83.0%) and Artificial Neural Networks (79.6%).