Title: Congruent fine-grained data mining model for large-scale medical data mining

Authors: J. Arthi Jaya Kumari; Muhammad Rukunddin Ghalib

Addresses: School of Computer Science & Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India ' School of Computer Science & Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

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.

Keywords: big data; classification learning; decision tree; data mining; medical data.

DOI: 10.1504/IJIPT.2022.125954

International Journal of Internet Protocol Technology, 2022 Vol.15 No.3/4, pp.148 - 160

Received: 11 Sep 2020
Accepted: 01 May 2021
Published online: 05 Oct 2022 *

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