The Integration of Hadoop and a Smart Utility Scoring with a Case-Based Reasoning System for Managing Large and Complex Medical Case Base

In case-based reasoning structure, the quality and the complexity of the case data play a significant role in searching, retrieving, updating, and holding distinct case data at or from distinct databases for various purposes. However, managing the very complex and large case-data sets is not easy. Furthermore, the complex algorithm for maintaining case-based reasoning (CBR) structure makes it more critical and increases the time complexity considerably when the dataset is large and complex. Consequently, distinct available techniques related to the maintenance of the CBR system concentrate on deleting the less important and very complex case data to reduce the number of cases. At the same time, it reduces the efficiency and effectiveness of handling the CBR system. Hence, this research employs an integrated platform with the combination of the Hadoop parallel platform and an intelligent utility scoring mechanism. The smart indexing system and the parallel processing capacity of Hadoop reduce the time complexity for processing large and complex datasets. The distributed storage capability effectively manages the data repository and retrieval system for enormous case datasets without compromising case data. The intelligent utility scoring system employs supervised learning and a unique CBR system to quickly and efficiently retrieve the effective solution for each particular case. Furthermore, Hadoop offers a distributed structure that helps the user to access the case data and update the database through the network. This research uses distinct healthcare text-based datasets for testing the performances of the proposed integrated technique in different aspects. The experimental results show the superiorities in producing a higher percentage of accuracy with consuming less Retrieval time over other related techniques. It further shows its time efficiency by offering higher throughput and lower Cyclomatic Complexity with lower read and write Latency Times. Finally, the performances of the proposed technique in these various aspects have been compared with distinct existing techniques and the performances of various databases in managing large and complex medical data to establish its superiority over them.