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Paperdoi:10.1371/journal.pdig.0001687

Demographic subgroup-aware analysis of blood pressure predictive models

Venue listed2026Added Oct 16, 2026

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Authors: Blessing Kolawole, Sinuo Fan, Yingjie Lao, Valencia Joyner Koomson (OpenAlex / PLOS)

PLOS Digital Health, 2026-10-15

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CC BY 4.0

TL;DR

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  • Form factor: clinical_finger

Abstract

Blood pressure (BP) is an important cardiovascular health indicator, usually monitored non-invasively with a sphygmomanometer, which can be bulky and inconvenient for real-time monitoring. Blood pressure estimation using photoplethysmography (PPG) waveform analysis offers a promising alternative for intermittent or continuous cuff-less BP monitoring. However, changes in vascularization due to individual physiology and age can directly affect the morphology of the PPG waveform. Thus, prediction accuracy requires a demographic-aware approach. To address these challenges, we trained three conventional ML models for BP prediction and conducted a subgroup-aware analysis to evaluate model performance across individual and intersectional demographic groups. Additionally, we show that signal quality assessment is a pivotal step in the signal-processing pipeline before feature extraction to mitigate external perturbations (e.g., motion artifacts) in the PPG waveform. We achieve a mean absolute error (MAE) of 3.36 mmHg for systolic blood pressure (SBP) and 1.76 mmHg for diastolic BP (DBP) with models trained when signal quality assessment is incorporated.

Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: http://creativecommons.org/licenses/by/4.0/

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Tasks & signals

Tasks

Blood pressureSignal quality

Signals

PPGBP

Keywords

Cuffless BPFairnessSubgroup AnalysisIntersectionalSQIMIMIC IIIICUClassical ML

Datasets used

How it works

How Method works: Setup → Method → TargetSetup: Signals (PPG, ABP); Form factor (clinical_finger). Method: Method. Target: Blood pressure; Signal quality.SetupSignals: PPG, ABPSignalsPPG, ABPForm factor: clinical_fingerForm factorclinical_fingerMethodMethodTargetBlood pressureBlood pressureSignal qualitySignal quality

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Kolawole, B., Fan, S., Lao, Y., & Koomson, V. J. (2026). Demographic subgroup-aware analysis of blood pressure predictive models. PLOS Digital Health. https://doi.org/10.1371/journal.pdig.0001687
Kolawole, Blessing, Sinuo Fan, Yingjie Lao, and Valencia Joyner Koomson. 2026. "Demographic subgroup-aware analysis of blood pressure predictive models." PLOS Digital Health. https://doi.org/10.1371/journal.pdig.0001687.
Kolawole, B., Fan, S., Lao, Y. and Koomson, V. J. (2026) 'Demographic subgroup-aware analysis of blood pressure predictive models', PLOS Digital Health. doi: 10.1371/journal.pdig.0001687.
Kolawole, Blessing, et al. "Demographic subgroup-aware analysis of blood pressure predictive models." PLOS Digital Health, 2026. https://doi.org/10.1371/journal.pdig.0001687.
B. Kolawole, S. Fan, Y. Lao, and V. J. Koomson, "Demographic subgroup-aware analysis of blood pressure predictive models," PLOS Digital Health, 2026, doi: 10.1371/journal.pdig.0001687.
@article{kolawole2026demographic,
  title         = {{Demographic subgroup-aware analysis of blood pressure predictive models}},
  author        = {Kolawole, Blessing and Fan, Sinuo and Lao, Yingjie and Koomson, Valencia Joyner},
  year          = {2026},
  journal       = {PLOS Digital Health},
  doi           = {10.1371/journal.pdig.0001687}
}

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