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Divide and recombine approaches for fitting logistic regression to large-scale health surveillance data: application to diabetes risk prediction in BRFSS

Crossref DOI link: https://doi.org/10.1038/s41598-026-46927-7

Published Online: 2026-04-03

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Nayem, Md. Mahadi Hassan

Biswas, Soma Chowdhury
License Information

Text and Data Mining valid from 2026-04-03

Accepted Manuscript valid from 2026-04-03
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Article History

Received: 10 December 2025

Accepted: 28 March 2026

First Online: 3 April 2026

Declarations

:

: The authors declare no competing interests.

: This study utilized publicly available de-identified survey data from the Behavioral Risk Factor Surveillance System (BRFSS). All BRFSS procedures were approved by relevant institutional review boards at the Centers for Disease Control and Prevention and participating state health departments. All participants provided informed consent prior to participation. The current secondary data analysis was conducted in accordance with ethical standards for human subjects research and did not require additional IRB approval as it used publicly available, de-identified data.

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