Open Access

ARTICLE

Machine Learning for Detecting Blood Transfusion Needs Using Biosignals

Hoon Ko¹, Chul Park², Wu Seong Kang³, Yunyoung Nam⁴, Dukyong Yoon⁵, Jinseok Lee^1,*

1 Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, 17104, Korea
2 Department of Internal Medicine, Wonkwang University School of Medicine, Iksan, 54538, Korea
3 Department of Trauma Surgery, Jeju Regional Trauma Center, Cheju Halla General Hospital, Jeju, 63127, Korea
4 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea
5 Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, 03722, Korea

* Corresponding Author: Jinseok Lee. Email:

Computer Systems Science and Engineering 2023, 46(2), 2369-2381. https://doi.org/10.32604/csse.2023.035641

Received 29 August 2022; Accepted 08 December 2022; Issue published 09 February 2023

Abstract

Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life. For those patients requiring blood, blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous line. However, detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed, such as internal bleeding. This study considered physiological signals such as electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure, oxygen saturation (SpO2), and respiration, and proposed the machine learning model to detect the need for blood transfusion accurately. For the model, this study extracted 14 features from the physiological signals and used an ensemble approach combining extreme gradient boosting and random forest. The model was evaluated by a stratified five-fold cross-validation: the detection accuracy and area under the receiver operating characteristics were 92.7% and 0.977, respectively.

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Keywords

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