Title: Total interpretive structural modelling of machine learning enablers in the healthcare system
Authors: Pooja Gupta; Ritika Mehra
Addresses: School of Computing, DIT University, Dehradun, India ' School of Computing, DIT University, Dehradun, India
Abstract: The primary objective of this research is to build a total interpretive structural model of different enablers, vital to implement machine learning in the healthcare system. This study begins by implementing the progressive methodology of TISM to investigate the mutual dependence among ML enablers in the healthcare system. Further, the classification of enablers has been done based upon the driving power and dependence. A structural model of ML enablers has also been developed using the TISM procedure. Ten enablers of ML implementation have been recognised from the literature and experts' opinions. TISM is applied to develop a six-level hierarchical structural model. The proposed study encourages decision-makers to focus on the necessary steps to implement these enablers. Enablers at the bottom of the TISM hierarchy are the ones with reliable driving power and these the lowest level enablers need more consideration from the top administration.
Keywords: machine learning; interpretive structural modelling; healthcare; MICMAC; total interpretive structural modelling; medical models; driving power; dependence; enablers.
International Journal of Applied Decision Sciences, 2022 Vol.15 No.3, pp.265 - 284
Received: 20 Jul 2020
Accepted: 02 Jan 2021
Published online: 04 May 2022 *