Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach

Currently, big data is considered one of the most significant areas of research and development. The advancement in technologies along with the involvement of intelligent and automated devices in each field of development leads to huge generation, analysis, and the recording of information in the network. Though a number of schemes have been proposed for providing accurate decision-making while analyzing the records, however, the existing methods lead to massive delays and difficulty in the management of stored information. Furthermore, the excessive delays in information processing pose a critical challenge to making accurate decisions in the context of big data. The aim of this paper is to propose an effective approach for accurate decision-making and analysis of the vast volumes of data generated by intelligent devices in the healthcare sector. The processed and managed records can be stored and accessed in a systematic and efficient manner. The proposed mechanism uses the hybrid of ensemble learning along with blockchain for fast and continuous recording and surveillance of information. The recorded information is analyzed using several existing methods focusing on various measurement outcomes. The results of the proposed technique are compared with existing techniques through various experiments that demonstrate the efficiency and accuracy of this technique.