often necessary in cardiovascular, orthopedic (e.g., hip and knee joint replacement), gynecological (e.g., radical hysterectomy), and urological (e.g., radical prostatectomy) surgical procedures [1]. However, blood transfusion may induce some risks, such as transmission of bacterial or viral infections. Therefore, the World Health Organization (WHO) urges member states to utilize transfusion alternatives and develop individualized Patient Blood Management (PBM) programs to reduce transfusion needs. The three pillars of PBM are as follows: (i) detection and treatment of preoperative anemia; (ii) reduction in perioperative RBC loss; and (iii) harnessing and optimizing the patient-specific physiological reserve of anemia (including restrictive hemoglobin transfusion triggers) [2]. To prevent excessive blood loss during surgery, preoperative blood preparation is often necessary in certain departments and disciplines. Previous studies have identified that more than 60-70% of prepared blood products go unused [3]. A further investigation of this trend has revealed that most physicians often determine whether a transfusion is necessary on the basis of their clinical experience, which results in a waste of related medical resources. Furthermore, it can impose a burden on medical staff. For example, two nursing personnel must perform joint assessments to facilitate accurate blood preparation. In addition, unnecessary requests for blood can put pressure on blood bank personnel to ensure the accurate management and storage of blood. Accordingly, enhancing the prediction accuracy of whether an intraoperative blood transfusion is necessary may effectively resolve current problems in clinical practice. The guideline of the Maximum Surgical Blood Order Schedule (MSBOS)-the amount of blood to be cross-matched for specific elective operations-has been used to rationalize the number of units of blood routinely cross-matched for elective surgical procedures and has concomitantly reduced the unnecessary use of blood [4; 5; 6; 7]. However, some primary concerns regarding the MSBOS are that the recommendations are often outdated, based on opinion, do not include recently developed surgical procedures, and are not based on institution-specific blood utilization data. Previous studies on intraoperative blood transfusion [8; 9; 10; 11; 12] have focused primarily on variables related to patients' physiological characteristics (e.g., weight, age, and sex) [13] and medication history (e.g., whether the patient is receiving nonsteroidal anti-inflammatory drugs or blood thinners) [10], the type and duration of the surgical procedure [14; 15], the use of tranexamic acid [16; 17], and whether patients have a history of cardiopulmonary disease [18]. A review of the literature revealed that most studies on related topics have investigated patients in the United States and Europe, but no study has addressed patients in Asian countries. In addition, these studies have mainly employed statistical analyses, whereas supervised learning algorithms have rarely been employed in their analysis of related variables. Moreover, no viable prediction model has been proposed regarding the clinical application of blood transfusions. According to the electronic medical records (EMRs) collected for this study, orthopedic surgery requires the highest volume of blood, the most frequent transfusions, and incurs the highest cost from unused blood products, suggesting that the prediction accuracy in orthopedic departments requires significant improvement. Therefore, to prevent unnecessary blood preparation, the aim of this study is to develop a prediction model to determine whether an orthopedic surgery patient requires a preoperative blood preparation for an intraoperative or postoperative blood transfusion. To make the precision model applicable in clinical practice, we only consider the set of independent variables (IVs) which can be retrieved before the onset of surgery.