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Predictive Analytics for Blood Supply Chain Management and Data Security in Healthcare System
  • +3
  • Vinusha Muthukudaarachchi,
  • Ashini Pushmika,
  • Yuthishka Wijesekara,
  • Tharushi Naragala,
  • Pradeep K.W. Abeygunawardhana,
  • Rangi Liyanage
Vinusha Muthukudaarachchi
Sri Lanka Institute of Information Technology
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Ashini Pushmika
Sri Lanka Institute of Information Technology
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Yuthishka Wijesekara
Sri Lanka Institute of Information Technology
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Tharushi Naragala
Sri Lanka Institute of Information Technology
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Pradeep K.W. Abeygunawardhana
Sri Lanka Institute of Information Technology

Corresponding Author:[email protected]

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Rangi Liyanage
Sri Lanka Institute of Information Technology
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Abstract

The Web-Based Blood Bank and Donation Management System (BBDMS) presents a comprehensive solution to optimize blood donation processes by integrating data security, predictive analytics powered by machine learning, and efficient management strategies. With a focus on safeguarding sensitive information and enhancing donor recruitment through ML-driven predictive models, the BBDMS utilizes encryption and access controls to ensure data integrity. Leveraging advanced data analytics and machine learning, the system predicts potential blood donors based on factors like age and location, improving recruitment efforts. Streamlining blood camp organization and appointment scheduling, the BBDMS enhances donor engagement and satisfaction. Moreover, by accurately forecasting blood demand using machine learning techniques and optimizing stock management, the system aids in efficient emergency response. Overall, the BBDMS transforms blood donation processes by enhancing security, prediction accuracy using machine learning, resource allocation, and emergency preparedness, contributing to improved public health outcomes and the effectiveness of blood banks and organizations.