AN AI-POWERED BLOOD BANKMANAGEMENT SYSTEM

Abstract

Blood bank management systems face critical challenges in demand forecasting, donor matching, and emergency response coordination. This paper presents Life Link 2.0, an AI powered blood bank management system that addresses these challenges through machine learning algorithms implemented in Python. The system employs Random Forest regression for demand forecasting, LSTM neural networks for time-series prediction, and computer vision models for document verification. Our hybrid architecture integrates Python AI services (Fast API, Scikit-learn 1.3.0, TensorFlow 2.13.0) with PHP backend and MySQL database, serving multiple user roles. The system processes donation records and demonstrates improved emergency response through intelligent donor matching algorithms. Experimental evaluation shows significant improvements in operational efficiency and user experience. The modular architecture supports real-time model updates and continuous learning. This research contributes novel machine learning approaches for healthcare resource optimization and demonstrates practical AI integration in critical healthcare systems