Congruent fine-grained data mining model for large-scale medical data mining. Issue 3 (3rd October 2022)

• Record Type: Journal Article
• Authors: Kumari, J. Arthi Jaya; Ghalib, Muhammad Rukunddin
• Is Part Of: International journal of internet protocol technology. Volume 15:Issue 3/4(2022)
• Page Start: 148
• Page End: 160
• Publication Date: 2022-10-03
• Subjects: big data -- classification learning -- decision tree -- data mining -- medical data; File Transfer Protocol (Computer network protocol) -- Periodicals; Multicasting (Computer networks) -- Periodicals; 004.678
• Languages: English
• ISSNs: 1743-8209

Abstract

Electronic medical data management is eased with the integration of communication technologies and the cloud/Internet of Things (IoT) platform in recent years. The organisation and mining of the data from massive repositories is a complex and time-consuming process. However, the exploitation of such massive information requires large-scale analytical procedures by accounting its significance. This article introduces Congruent Fine-grained Data Mining (CFDM) model for reducing the complexities in large-scale medical data handling. This model identifies the independent and relation-based repositories for matching the request queries in the data retrieval process. By using a classification decision-tree, the identifications are performed to improve the retrieval rate. In this classification tree, the independent and relation-based data are first analysed for their matching consistencies. By pursuing this process, the non-matching query-data satisfying the relevance condition are grouped into a new relationship based classification. This helps to improve the matching and retrieval rate preciously in the consecutive mining instances. The proposed model improves retrieval responses, retrieving time, complexity and data availability.