Open Access Open Access  Restricted Access Subscription Access

BLOODMAP AI: AN AI-DRIVEN FRAMEWORK FOR INTELLIGENT BLOOD DONOR MATCHING AND BLOOD DEMAND FORECASTING

Mr. K. Gowtham, Mrs. N. Gayathri

Abstract


Timely identification of suitable blood donors is a critical operational challenge because donor availability, blood-group compatibility, geographical distance, and response behavior can change rapidly. This paper presents BloodMap AI, a web-based intelligent blood donation management framework that combines compatibility-aware donor ranking, location-based scoring, blood-demand forecasting, donor eligibility rules, persistent data management, and an extensible machine-learning/MLOps service. The frontend is implemented with React, Vite, TypeScript, and Supabase, while a Python FastAPI service provides protected matching, forecasting, retraining, health, and Prometheus monitoring endpoints. The donor matching component first removes incompatible donors and then considers blood-group compatibility, Haversine distance, and current availability; an XGBoost-based ranking model can be loaded when a trained model artifact is present, with a deterministic rules-based fallback for service resilience. The demand-forecasting interface aggregates actual blood-request records and produces a short-horizon forecast with lower and upper bounds. The repository also includes data extraction, preprocessing, model-training, model registration, drift monitoring, automated tests, and GitHub Actions workflows for continuous integration and weekly retraining. Functional validation of the uploaded project passed six unit tests covering compatibility, donor ranking, and forecasting behavior. The work demonstrates a practical architecture for AI-assisted blood-resource coordination while identifying the need for larger real-world datasets and statistically validated model benchmarks.


Full Text:

PDF

References


SeleniumHQ, “Selenium Documentation,” Selenium Project.

FastAPI, “FastAPI Documentation,” FastAPI Project.

Supabase, “Supabase Documentation,” Supabase.

XGBoost Developers, “XGBoost Documentation,” XGBoost Project.

F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.

M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” 2016.

T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.

S. Breck et al., “The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction,” IEEE Big Data, 2017.

N. P. Jouppi et al., “Machine Learning Systems and MLOps: A Practical Perspective,” background literature to be finalized according to the selected journal style.


Refbacks

  • There are currently no refbacks.