Comparative Evaluation of Classification Indexes and Outlier Detection of Microcytic Anaemias in a Portuguese Sample

Abstract

Anaemia is often caused by a nutritional problem or by genetic diseases. The world prevalence of anaemia is estimated to be 24.8%, strengthening the need for appropriate discrimination methods between the different types of this disease, an essential step to choosing the best treatment and offering genetic counselling. Several indexes based on haematological features have been proposed to address the challenge of microcytic anaemias classification. However, they have not been tested extensively nor optimised for different countries. Here we test existing binary classification indexes in a Portuguese sample of 390 patients diagnosed with microcytic anaemia and propose novel classification methods to discriminate between the disease classes. We show that existing indexes for the binary classification of Iron Deficiency Anaemia (IDA) and β -thalassaemia trait are well adapted to this sample, with RDWI (red cell distribution width index) achieving a median accuracy of 95.4%, a performance we were also able to achieve using Random Forests. The multi-class classification was also developed to discriminate between three microcytic anaemias and healthy subjects, presenting a median accuracy of 93.0%. In addition, we developed a semi-automatic method to identify outliers, which were shown to correspond to subjects with unexpected features given their class and who may correspond to clinical misclassification that require further analysis. The results illustrate that it is possible to achieve excellent performance using just the information obtained through an affordable Complete Blood Count test, thus highlighting the potential of artificial intelligence in classifying microcytic anaemias.