Knowledge-based mining with the game-theoretic rough set approach to handling inconsistent healthcare data. (13th February 2022)

• Record Type: Journal Article
• Authors: Singh, Abhay Kumar; Ghalib, Muhammad Rukunuddin
• Is Part Of: International journal of high performance systems architecture. Volume 10:Number 3/4(2021)
• Page Start: 174
• Page End: 184
• Publication Date: 2022-02-13
• Subjects: healthcare data -- machine learning knowledge-based mining -- data analysis -- latency -- complexity -- accuracy; Computer architecture -- Periodicals; Computer systems -- Periodicals; High performance computing -- Periodicals; 004.205
• Languages: English
• ISSNs: 1751-6528

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.