Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks

作者：Hong-yun Wu, Zhenggang Li, Xinkai Sun, Weimin Bai, An-di Wang, Yuchi Ma, Ren-hua Diao, Eng-yong Fan, Fang Zhao, Yunqi Liu, Yi-zhou Hong, Ming-hua Guo, Hui Xue, Wenbiao Liang · 发表于：Scientific Reports · 年份：2022 · DOI：10.1038/s41598-022-21215-2 · 被引用次数：20 · 研究领域：Blood donation and transfusion practices、Dietary Effects on Health、AI in Service Interactions

Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoos...