Enhancing Big Data Management for Elderly Care Through a Blockchain-Empowered Deep Reinforcement Learning Model

The surging demand for elderly care services, propelled by the global population’s demographic shift, necessitates innovative approaches for efficient data management and decision-making. This paper introduces a novel framework that synergizes blockchain technology with a deep reinforcement learning (DRL) model to elevate the integrity, security, and effectiveness of big data management in elderly care. Our proposed model offers dual benefits: the decentralized nature of blockchain ensures the secure storage and exchange of patient data, countering potential breaches and unauthorized accesses. This is particularly crucial given the sensitive nature of elderly patients’ information. Additionally, using smart contracts, automated and transparent caregiving decisions can be executed with-out intermediaries, thus ensuring trust among stakeholders. On the other hand, the DRL model dynamically learns and adapts to individual elderly care requirements by processing vast amounts of data, offering personalized care recommendations. By integrating it with blockchain, we not only ensure data integrity but also optimize decision-making in real-time, accommodating the evolving needs of the elderly population. A series of experiments were conducted using real-world data to validate the effectiveness of our proposed model. Results highlighted significant improvements in data security, reduction in fraudulent activities, and efficient, tailored caregiving decisions. The holistic integration of blockchain and DRL presents a revolutionary stride in elderly care, offering an optimized, secure, and personalized caregiving paradigm. This paper also discusses the potential challenges of implementing this model and offers directions for future research.