Artificial intelligence-driven diagnosis of β-thalassemia minor & iron deficiency anemia using machine learning models

Abstract

Background: Iron deficiency anemia (IDA) and b-thalassemia minor (BTM) are the two most common causes of microcytic anemia, and although these conditions do not share many symptoms, differential diagnosis by blood tests is a time-consuming and expensive process. CBC can be used to diagnose anemia, but without advanced techniques, it cannot differentiate between iron deficiency anemia and BTM. This makes the differential diagnosis of IDA and BTM costly, as it requires advanced techniques to differentiate between the two conditions. This study aims to develop a model to differentiate IDA from BTM using an automated machine-learning method using only CBC data. Methods: This retrospective study included 396 individuals, consisting of 216 IDAs and 180 BTMs. The work was divided into three parts. The first section focused on the individual effects of hematological parameters on the differentiation of IDA and BTM. The second part discusses traditional methods and discriminant indices used in diagnosis. In the third section, models developed using artificial neural networks (ANN) and decision trees are analysed and compared with the methods used in the first two sections. Results: The studyžs conclusions are presented in three parts. The first part of the results suggests that MCV and RBC are the most effective predictors of discrimination between the two conditions. The second part of the results suggests that the effects of discriminant indices on the differentiation of BTM and IDA were similar. However, using G & K and RDWI instead of other discriminant indices for BTM and IDA greatly increases differentiation. The third section of the results reveals that machine learning models such as ANN are more powerful than traditional discriminant indices. Conclusion: This study recommends an artificial neural network-based system to differentiate the two states. In conclusion, our results show that the ANN method performs better than the existing methods. Although other approaches have been effective, artificial intelligence can better predict the presence of various hemoglobin variants than traditional statistical approaches. This differentiation is important because it can have important medical implications on patient care, family planning, and genetic counselling related to health. The neural network model can also save time, cost less, and make diagnosis easier.