PMID- 34267466 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20220716 IS - 0971-4502 (Print) IS - 0974-0449 (Electronic) IS - 0971-4502 (Linking) VI - 37 IP - 3 DP - 2021 Jul TI - Role of Red Cell Indices in Screening for Beta Thalassemia Trait: an Assessment of the Individual Indices and Application of Machine Learning Algorithm. PG - 453-457 LID - 10.1007/s12288-020-01373-x [doi] AB - Antenatal screening for beta thalassemia trait (BTT) followed by counseling of couples is an efficient way of thalassemia control. Since high performance liquid chromatography (HPLC) is costly, other cost-effective screening methods need to be devised for this purpose. The present study was aimed at evaluating the utility of red cell indices and machine learning algorithms including an artificial neural network (ANN) in detection of BTT among antenatal women. This cross-sectional study included all antenatal women undergoing thalassemia screening at a tertiary care hospital. Complete blood count followed by HPLC was performed. Receiver operating characteristic (ROC) curve analysis was performed for obtaining optimal cutoff for each of the indices with determination of test characteristics for detection of BTT. Machine learning algorithms including C4.5 and Naive Bayes (NB) classifier and a back-propagation type ANN including the red cell indices was designed and tested. Over a period of 15 months, 3947 patients underwent thalassemia screening. BTT was diagnosed in 5.98% of women on the basis of HPLC. ROC analysis yielded the maximum accuracy of 63.8%, sensitivity and specificity of 66.2% and 63.7%, respectively for Mean corpuscular hemoglobin concentration (MCHC). The C4.5 and NB classifier had accuracy of 88.56%-82.49% respectively while ANN had an overall accuracy of 85.95%, sensitivity of 83.81%, and specificity of 88.10% in detection of BTT. The present study highlights that none of the red cell parameters standalone is useful for screening for BTT. However, ANN with combination of all the red cell indices had an appreciable sensitivity and specificity for this purpose. Further refinements of the neural network can provide an appropriate tool for use in peripheral settings for thalassemia screening. CI - (c) Indian Society of Hematology and Blood Transfusion 2020. FAU - Jahan, Aarzoo AU - Jahan A AD - Department of Pathology, Hindu Rao Hospital, Delhi, India. GRID: grid.414109.9. ISNI: 0000 0004 1791 9689 FAU - Singh, Garima AU - Singh G AD - Department of Pathology, Hindu Rao Hospital, Delhi, India. GRID: grid.414109.9. ISNI: 0000 0004 1791 9689 FAU - Gupta, Ruchika AU - Gupta R AUID- ORCID: 0000-0003-0235-063X AD - Division of Cytopathology, ICMR-National Institute of Cancer Prevention and Research, Noida, India. GRID: grid.501268.8 FAU - Sarin, Namrata AU - Sarin N AD - Department of Pathology, Hindu Rao Hospital, Delhi, India. GRID: grid.414109.9. ISNI: 0000 0004 1791 9689 FAU - Singh, Sompal AU - Singh S AD - Department of Pathology, Hindu Rao Hospital, Delhi, India. GRID: grid.414109.9. ISNI: 0000 0004 1791 9689 LA - eng PT - Journal Article DEP - 20201027 PL - India TA - Indian J Hematol Blood Transfus JT - Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion JID - 9425818 PMC - PMC8239087 OTO - NOTNLM OT - Beta thalassemia trait OT - Machine learning algorithm OT - Red cell indices COIS- Conflict of interestThe authors confirm that there are no conflicts of interests to declare. EDAT- 2021/07/17 06:00 MHDA- 2021/07/17 06:01 PMCR- 2022/07/01 CRDT- 2021/07/16 06:33 PHST- 2020/07/03 00:00 [received] PHST- 2020/10/20 00:00 [accepted] PHST- 2021/07/16 06:33 [entrez] PHST- 2021/07/17 06:00 [pubmed] PHST- 2021/07/17 06:01 [medline] PHST- 2022/07/01 00:00 [pmc-release] AID - 1373 [pii] AID - 10.1007/s12288-020-01373-x [doi] PST - ppublish SO - Indian J Hematol Blood Transfus. 2021 Jul;37(3):453-457. doi: 10.1007/s12288-020-01373-x. Epub 2020 Oct 27.