Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications

Abstract

Remote Patient Monitoring Systems (RPMS) are vital for tracking patients' health outside clinical settings, such as at home or in long-term care facilities. Wearable sensors play a crucial role in these systems by continuously collecting and transmitting health data. However, selecting the optimal sensor is challenging due to the wide variety of available options and diverse patient needs. To address this paper, introduce score and accuracy functions for Triangular Fermatean Fuzzy Numbers (TFFNs) and propose a novel Triangular Fermatean Fuzzy Sugeno-Weber Weighted Average (TFFSWWA) aggregation operator. In this paper establish key properties of TFFSWWA, confirming its ability to manage fuzzy uncertainty effectively. Using TFFSWWA, we develop an improved Evaluation based on Distance from Average Solution (EDAS) method for multi-criteria group decision-making (MCGDM) under TFFN settings. A case study on wearable sensor selection demonstrates the proposed model's efficiency. We present an algorithm and a flowchart to guide the decision-making process, alongside a computational example that verifies the method's reliability. Sensitivity analysis and comparison with existing methods show that the proposed approach improves decision accuracy and stability, highlighting its practical utility in healthcare decision-making.

Keywords: Decision support system; Remote Patient Monitoring Systems; Sugeno–Weber aggregation operator; Triangular Fermatean fuzzy number.

Fig. 1

Space comparison of IFS, PFS and FFS.

Fig. 2

Graphical representation of TFFNs.

Fig. 3

Geometrical representation of MF and NMF of TFFNs.

Fig. 4

Flow chart of the proposed model for Wearable Sensors for RPMS, Boikanyo et al.. (Figure generated using Microsoft Office.).

Fig. 5

Graphical representation of comparison EDAS method and TFFSWWA operator, Boikanyo et al.. (Figure generated using Microsoft Office.)

Fig. 6

Graphical representation of Sensitivity analysis of the parameter .

Fig. 7

Graphical representation of Comparison analysis with existing method.