Abstract
Background: Currently, more than forty discrimination formulae based on red blood cell (RBC) parameters and some supervised machine learning algorithms (MLAs) have been recommended for β-thalassemia trait (BTT) screening. The present study was aimed to evaluate and compare the performance of 26 such formulae and 13 MLAs on antenatal woman data with a recently developed formula SCSBTT, which is available for evaluation in over seventy countries as an Android app, called SUSOKA [16]. Methods: A diagnostic database of 2942 antenatal females were collected from PGIMER, Chandigarh, India and was used for this analysis. The data set consists of hypochromic microcytic anemia, BTT, Hemoglobin E trait, double heterozygote for Hemoglobin S and BTT, heterozygote for Hemoglobin D Punjab and normal subjects. Performance of the formulae and the MLAs were assessed by Sensitivity, Specificity, Youden's Index, and AUC-ROC measures. A final recommendation was made from the ranking obtained through two Multiple Criteria Decision-Making (MCDM) techniques, namely, Simultaneous Evaluation of Criteria and Alternatives (SECA) and TOPSIS. Results: It was observed that Extreme Learning Machine (ELM) and Gradient Boosting Classifier (GBC) showed maximum Youden's index and AUC-ROC measures compared to all discriminating formulae. Sensitivity remains maximum for SCSBTT. K-means clustering and the ranking from MCDM methods show that SCSBTT, Shine & Lal and Ravanbakhsh-F4 formula ensures higher performance among all formulae. The discriminant power of some MLAs and formulae was found considerably lower than that reported in original studies. Conclusion: Comparative information on MLAs can aid researchers in developing new discriminating formulae that simultaneously ensure higher sensitivity and specificity. More multi-centric verification of the formulae on heterogeneous data is indispensable. SCSBTT and Shine & Lal formula, and ELM and GBC are recommended for screening BTT based on MCDM. SCSBTT can be used with certainty as a tangible cost-saving screening tool for mass screening for antenatal women in India and other countries.
Original language | English |
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Article number | 104866 |
Journal | International Journal of Medical Informatics |
Volume | 167 |
ISSN | 1386-5056 |
DOIs | |
Publication status | Published - Nov 2022 |
Bibliographical note
Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.Keywords
- Antenatal Women
- Diagnostic performance
- Multi-criteria decision-making
- Supervised machine learning algorithm
- β-Thalassemia carrier screening
- Diagnosis, Differential
- beta-Thalassemia/diagnosis
- Humans
- Machine Learning
- Pregnancy
- Algorithms
- Mass Screening
- Anemia, Iron-Deficiency/diagnosis
- Female
- Hemoglobin, Sickle
- Hemoglobin E