Authors:
Hakan Burak Karli
;
Eli Hilborn
and
Bige Deniz Unluturk
Affiliation:
Michigan State University, Electrical and Computer Engineering Department, East Lansing, MI, U.S.A.
Keyword(s):
Pulse Oximetry, SpO2 Accuracy, Racial Bias in Medical Devices, Machine Learning in Healthcare, Oximetry Calibration, Health Disparities.
Abstract:
This paper investigates the racial biases in pulse oximetry, focusing on the importance of noninvasive peripheral oxygen saturation (SpO2) measurements in classifying patient race and ethnicity. Using the publicly available BOLD dataset, our study applies various machine learning models to quantify the extent of bias in SpO2 readings. Initial analysis revealed significant inaccuracies for individuals with darker skin tones, highlighting broader health disparities. Further exploration with machine learning models assessed SpO2 as a predictive marker for race, uncovering that conventional oximetry may underestimate hypoxemia in non-White patients. Notably, the XGBoost model demonstrated superior performance, achieving baseline accuracy of 58.08% across the dataset with all races and 72.60% for only black and white patients included, while consis-tently identifying SpO2 as a significant factor in these disparities. Our findings demonstrate the necessity for recalibrating medical devices
to enhance their reliability and inclusivity, ensuring equitable health outcomes.
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