Performance analysis of machine learning algorithms and screening formulae for β–thalassemia trait screening of Indian antenatal women. (November 2022)

• Record Type: Journal Article
• Authors: Das, Reena; Saleh, Sarkaft; Nielsen, Izabela; Kaviraj, Anilava; Sharma, Prashant; Dey, Kartick; Saha, Subrata
• Is Part Of: International journal of medical informatics. Volume 167(2022)
• Publication Date: 2022-11
• Subjects: β-Thalassemia carrier screening -- Supervised machine learning algorithm -- Multi-criteria decision-making -- Antenatal Women -- Diagnostic performance; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2022.104866
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Hematological parameters of Indian antenatal women were evaluated to screen for BTT. 27 discrimination formulae and 13 machine learning algorithms were evaluated. SCSBTT and Shine & Lal formulae and ELM and GBC algorithms were found suitable. SUSOKA app could be used as a cost-saving screening tool in resource-poor countries. 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 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.