Title: Knowledge-based mining with the game-theoretic rough set approach to handling inconsistent healthcare data

Authors: Abhay Kumar Singh; Muhammad Rukunuddin Ghalib

Addresses: School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, India ' School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, India

Abstract: Healthcare data analysis played a crucial role in the medical industries for examining the medical data. Primarily, in innovative healthcare applications, a massive volume of data must be handled and processed to make clinical decisions. Therefore, machine learning and intelligent techniques are introduced in the healthcare data analysis field to improve data processing. This paper focuses on developing knowledge-based mining with a game-theoretic rough set (KM-GTRS) based healthcare data analysis process. The knowledge mining process able to handling the high-dimensional data and providing the data to the application-centric services. Here, the introduced game-theoretic rough set algorithm analyses the medical data and effectively controls the inconsistent and missing data. In addition to this, the method ensures the solutions for medical data analysis with minimum time. The sufficient identification of inconsistent data improves the overall medical analysis recognition accuracy. This process achieves the minimum analysis time, computation complexity, inconsistency, and service latency.

Keywords: healthcare data; machine learning knowledge-based mining; data analysis; latency; complexity; accuracy.

DOI: 10.1504/IJHPSA.2021.121019

International Journal of High Performance Systems Architecture, 2021 Vol.10 No.3/4, pp.174 - 184

Received: 14 Dec 2020
Accepted: 16 Mar 2021
Published online: 22 Feb 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article