TY - EJOU AU - Ko, Hoon AU - Park, Chul AU - Kang, Wu Seong AU - Nam, Yunyoung AU - Yoon, Dukyong AU - Lee, Jinseok TI - Machine Learning for Detecting Blood Transfusion Needs Using Biosignals T2 - Computer Systems Science and Engineering PY - 2023 VL - 46 IS - 2 SN - AB - 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. KW - Blood transfusion; ECG; PPG; pulse transit time; blood pressure; machine learning DO - 10.32604/csse.2023.035641