IRO Journals
Journal of Ubiquitous Computing and Communication Technologies

An Intelligent Blood Management System Using OPTICS Clustering and Real-Time Donor Tracking

Open Access
Published: 27 October, 2026
Pages: 396-410
  • Rosiya A.S. , Rosiya A.S.
    Students, Department of Artificial Intelligence And Data Science, NPR College of Engineering and Technology, Dindigul, India
    Students, Department of Artificial Intelligence And Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Niranjana S. , Niranjana S.
    Students, Department of Artificial Intelligence And Data Science, NPR College of Engineering and Technology, Dindigul, India
    Students, Department of Artificial Intelligence And Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Krisha C.R. , Krisha C.R.
    Students, Department of Artificial Intelligence And Data Science, NPR College of Engineering and Technology, Dindigul, India
    Students, Department of Artificial Intelligence And Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Preethi Monika K. Preethi Monika K.
    Assistant Professor, Department of Artificial Intelligence And Data Science, NPR College of Engineering and Technology, Dindigul, India
    Assistant Professor, Department of Artificial Intelligence And Data Science, NPR College of Engineering and Technology, Dindigul, India
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How to Cite

A.S., Rosiya, Niranjana S., Krisha C.R., and Preethi Monika K. 2026. “An Intelligent Blood Management System Using OPTICS Clustering and Real-Time Donor Tracking”. Journal of Ubiquitous Computing and Communication Technologies 8 (4): 396-410. https://doi.org/10.36548/jucct.2026.4.007.
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Keywords

Donor-Priority Classification
OPTICS Spatial Clustering
Blood-Group Compatibility Filtering
Inventory-First Verification
Geographical Donor Prioritization
Post-Acceptance Tracking
Intelligent Blood Management

Abstract

Effective blood donor selection involves the efficient prioritization of donors, compatibility checking, inventory validation, and spatial location-based donor identification. This research proposes an intelligent blood management system including four-class donor priority classification, eligibility and blood group validation, inventory first validation, spatial clustering using OPTICS algorithm, geographic prioritization, and donor tracking after acceptance. For donor priority classification, Blood Transfusion Service Center dataset comprising 748 donor data and four numerical attributes-recency, frequency, monetary value of blood volume, and time-has been used. The developed classification algorithm shows 0.95 precision, 0.93 recall, and 0.94 F1-score while the confusion matrix analysis shows that there is an effective segregation between the four classes of donor priorities having 91.37% classification accuracy. Furthermore, the results of multiclass ROC analysis confirm the discriminatory capability of the model having AUC values ranging from 0.95 to 0.99.

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