PMID- 36174416 OWN - NLM STAT- MEDLINE DCOM- 20221013 LR - 20221020 IS - 1872-8243 (Electronic) IS - 1386-5056 (Linking) VI - 167 DP - 2022 Nov TI - Performance analysis of machine learning algorithms and screening formulae for beta-thalassemia trait screening of Indian antenatal women. PG - 104866 LID - S1386-5056(22)00180-0 [pii] LID - 10.1016/j.ijmedinf.2022.104866 [doi] AB - 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 beta-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 SCS(BTT), 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 SCS(BTT). K-means clustering and the ranking from MCDM methods show that SCS(BTT), 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. SCS(BTT) and Shine & Lal formula, and ELM and GBC are recommended for screening BTT based on MCDM. SCS(BTT) can be used with certainty as a tangible cost-saving screening tool for mass screening for antenatal women in India and other countries. CI - Copyright (c) 2022 The Author(s). Published by Elsevier B.V. All rights reserved. FAU - Das, Reena AU - Das R AD - Department of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India. FAU - Saleh, Sarkaft AU - Saleh S AD - Department of Materials and Production, Aalborg University, DK 9220 Aalborg, Denmark. FAU - Nielsen, Izabela AU - Nielsen I AD - Department of Materials and Production, Aalborg University, DK 9220 Aalborg, Denmark. FAU - Kaviraj, Anilava AU - Kaviraj A AD - Department of Zoology, University of Kalyani, Kalyani 741235, India. FAU - Sharma, Prashant AU - Sharma P AD - Department of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India. FAU - Dey, Kartick AU - Dey K AD - Department of Mathematics, University of Engineering & Management, Kolkata 700160, India. FAU - Saha, Subrata AU - Saha S AD - Department of Materials and Production, Aalborg University, DK 9220 Aalborg, Denmark. LA - eng PT - Journal Article DEP - 20220916 PL - Ireland TA - Int J Med Inform JT - International journal of medical informatics JID - 9711057 RN - 0 (Hemoglobin, Sickle) RN - 9034-61-1 (Hemoglobin E) SB - IM MH - Algorithms MH - *Anemia, Iron-Deficiency/diagnosis MH - Diagnosis, Differential MH - Female MH - *Hemoglobin E MH - Hemoglobin, Sickle MH - Humans MH - Machine Learning MH - Mass Screening MH - Pregnancy MH - *beta-Thalassemia/diagnosis OTO - NOTNLM OT - Antenatal Women OT - Diagnostic performance OT - Multi-criteria decision-making OT - Supervised machine learning algorithm OT - beta-Thalassemia carrier screening COIS- Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could influenced the work reported in this paper. EDAT- 2022/09/30 06:00 MHDA- 2022/10/14 06:00 CRDT- 2022/09/29 18:25 PHST- 2022/06/03 00:00 [received] PHST- 2022/07/19 00:00 [revised] PHST- 2022/09/07 00:00 [accepted] PHST- 2022/09/30 06:00 [pubmed] PHST- 2022/10/14 06:00 [medline] PHST- 2022/09/29 18:25 [entrez] AID - S1386-5056(22)00180-0 [pii] AID - 10.1016/j.ijmedinf.2022.104866 [doi] PST - ppublish SO - Int J Med Inform. 2022 Nov;167:104866. doi: 10.1016/j.ijmedinf.2022.104866. Epub 2022 Sep 16.