Abstract

BackgroundThe hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT). Accurate discrimination between IDA and TT is an important issue and better methods are urgently needed. Although considerable RBC formulas and indices with various optimal cut-off values have been developed, distinguishing between IDA and TT is still a challenging problem due to the diversity of various anemic populations. To address this problem, it is desirable to develop an improved and automated prediction model for discriminating IDA from TT.MethodsWe retrospectively collected laboratory data of HMA found in Thai adults. Five machine learnings, including k-nearest neighbor (k-NN), decision tree, random forest (RF), artificial neural network (ANN) and support vector machine (SVM), were applied to construct a discriminant model. Performance was assessed and compared with thirteen existing discriminant formulas and indices.ResultsThe data of 186 patients (146 patients with TT and 40 with IDA) were enrolled. The interpretable rules derived from the RF model were proposed to demonstrate the combination of RBC indices for discriminating IDA from TT. A web-based tool ‘ThalPred’ was implemented using an SVM model based on seven RBC parameters. ThalPred achieved prediction results with an external accuracy, MCC and AUC of 95.59, 0.87 and 0.98, respectively.ConclusionThalPred and an interpretable rule were provided for distinguishing IDA from TT. For the convenience of health care team experimental scientists, a web-based tool has been established at http://codes.bio/thalpred/ by which users can easily get their desired screening test result without the need to go through the underlying mathematical and computational details.

Highlights

  • The hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT)

  • Multiple formulas and indices have been proposed for such discrimination including the Bessman index (BI) [8], Ehsani formula (EF) [9], England & Fraser index (E&F) [10], Green & King index (G&K) [11], Mentzler index (MI) [12], Red Cell Distribution Width index (RDWI) [13], Ricerca index (RI) [14], Shine and Lal index (S&L) [15], Siridah index (SI) [16], Srivastava formula (SF) [17], Sirachainan formula (SiF) [18], Kandhro 1 formula (KF1) [19] and Kandhro 2 formula (KF2) [19]

  • The outputs of two analyses were consistent. These suggested that all Red blood cell (RBC) indices may be important to discriminate between patients with IDA and those with TT

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Summary

Introduction

The hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT). Considerable RBC formulas and indices with various optimal cut-off values have been developed, distinguishing between IDA and TT is still a challenging problem due to the diversity of various anemic populations. Hb testing via high performance liquid chromatography or capillary electrophoresis to detect abnormal Hb levels or DNA analysis are the assays commonly used for TT diagnosis [5,6,7] These latter techniques are not available in routine laboratories as they require special machines, and are time- and cost-consuming. When patients were assumed to be TT or IDA, clinician often prescribed the cassette of hematological tests covering both TT and IDA diagnosis These laboratory workups consume personnel work load and governmental budget. Multiple formulas and indices have been proposed for such discrimination including the Bessman index (BI) [8], Ehsani formula (EF) [9], England & Fraser index (E&F) [10], Green & King index (G&K) [11], Mentzler index (MI) [12], Red Cell Distribution Width index (RDWI) [13], Ricerca index (RI) [14], Shine and Lal index (S&L) [15], Siridah index (SI) [16], Srivastava formula (SF) [17], Sirachainan formula (SiF) [18], Kandhro 1 formula (KF1) [19] and Kandhro 2 formula (KF2) [19]

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