Research on the Generalization of a Blood Donor Recruitment Framework Based on Machine Learning

Background Recruiting blood donors is essential for public health; however, existing traditional methods are often inefficient, due to its reliance on large-scale messaging campaigns to achieve acceptable success rates. Recent studies have shown that machine learning–based recruitment strategies can significantly outperform traditional approaches. Methods Recruitment framework was developed and validated using donation and SMS data from Nanjing, China, then fine-tuned with 10% of data from Suzhou and Yangzhou, thereby demonstrating cross-center applicability. Optimized multi-layer perceptron (MLP) and random forest (RF) models were prospectively compared with conventional recruitment approaches across all three cities. Results In Nanjing, the recall reached 0·72 for MLP and 0·70 for RF. Fine-tuned models generalized well, achieving recall of 0·63 and 0·67 in Suzhou, and 0·58 and 0·63 in Yangzhou. Further optimization led to improved recall rates of 0·70 and 0·77 in Suzhou, and 0·68 and 0·64 in Yangzhou. Compared to traditional methods, recruitment success increased by 408·28% in Nanjing, 25·19% in Suzhou, and 47·31% in Yangzhou respectively, while SMS volume decreased by 18·71%, 121·52%, and 138·75%. Efficiency per SMS increased by 9·54% in Nanjing, 53·61% in Suzhou, and 38·98% in Yangzhou. Conclusions ML–driven recruitment frameworks demonstrated strong generalizability across cities, substantially improving recruitment efficiency while reducing costs. These findings support their adoption to enhance donor recruitment and ensure a more stable blood supply.