Multi-criteria decision making to validate performance of RBC-based formulae to screen $$\beta$$ β -thalassemia trait in heterogeneous haemoglobinopathies

Autor: Atul Kumar Jain, Prashant Sharma, Sarkaft Saleh, Tuphan Kanti Dolai, Subhas Chandra Saha, Rashmi Bagga, Alka Rani Khadwal, Amita Trehan, Izabela Nielsen, Anilava Kaviraj, Reena Das, Subrata Saha

Publikováno v: BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-12 (2024)

Abstract Background India has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cost-effective digital t

Externí odkaz: https://doaj.org/article/093ee33f26c247f9adee2a775b15a298

Performance analysis of machine learning algorithms and screening formulae for β–thalassemia trait screening of Indian antenatal women

Autor: Reena Das, Sarkaft Saleh, Izabela Nielsen, Anilava Kaviraj, Prashant Sharma, Kartick Dey, Subrata Saha

Publikováno v: Das, R, Saleh, S, Nielsen, I, Kaviraj, A, Sharma, P, Dey, K & Saha, S 2022, ' Performance analysis of machine learning algorithms and screening formulae for β–thalassemia trait screening of Indian antenatal women ', International Journal of Medical Informatics, vol. 167, 104866 . https://doi.org/10.1016/j.ijmedinf.2022.104866

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf71556d48edc8a89fd8e2a2e67c977c
https://vbn.aau.dk/ws/files/495551658/1_s2.0_S1386505622001800_main.pdf