JMIR Publications

ABSTRACT

Background:

Parkinson’s Disease (PD) is reported to be among the most prevalent neurodegenerative diseases globally, presenting ongoing challenges and increasing burden on healthcare systems. In an effort to support PD patients, their carers, and the wider healthcare sector to manage this incurable condition, focus has begun to shift away from traditional treatments. One of the most contemporary treatments includes the prescribing of Assistive Technologies (ATs), which are viewed as a way to promote independent living and deliver remote care. The uptake of these ATs is varied, however, with some users not ready or willing to all forms of AT and others only willing to accept low-technology solutions. Consequently, to manage both the demands on resources and the efficiency with which ATs are deployed, new approaches are needed to automatically assess or predict a user’s likelihood to accept and adopt a particular AT in advance of it being prescribed. It is then necessary to use classification algorithms supporting effective AT allocation. From a computational perspective, different classification algorithms and selection criteria can be considered to address this need.

Objective:

This paper presents a novel hybrid MCDM approach to support classifier selection in technology adoption processes involving PD patients.

Methods:

First, the Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) was implemented to calculate the relative priorities of criteria and sub-criteria considering experts’ knowledge and uncertainty. Second, the Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL) was applied to evaluate the cause-effect relations among criteria/sub-criteria. Finally, the Combined Compromise Solution (CoCoSo) was used to rank the candidate classifiers based on their capability to model the technology adoption.

Results:

A case study involving a mobile smartphone solution was employed to validate the proposed methodology. Structure (F5) was identified as the factor with the highest relative priority (global weight = 0.214) while Adaptability (F4) (D - R = 1.234) was found to be the most influencing aspect when selecting classifiers for technology adoption in PD patients. In this case, the most appropriate algorithm for supporting technology adoption in PD patients was the A3 - J48 Decision Tree (M3 = 2.5592).

Conclusions:

The IF-AHP-IF-DEMATEL-CoCoSo approach helps to identify classification algorithms that cannot only discriminate between good and bad adopters of assistive technologies within the Parkinson’s population but also consider technology-specific features like design, quality, and compatibility that make these classifiers easily implementable by clinicians in the healthcare system.