A Fuzzy Collaborative Approach for Evaluating the Suitability of a Smart Health Practice
Abstract
:1. Introduction
2. Previous Work
3. Methodology
3.1. Deriving Fuzzy Weights for Each Decision Maker Using ACO
3.2. Aggregating the Fuzzy Weights by All Decision Makers Using FI
- (i)
- iffor some g: If a decision maker is absolutely important, then the value ofis determined solely by the decision maker.
- (ii)
- if∀ k: If all decision makers are equally important, then the value ofis determined by the consensus among the decision makers.
- (iii)
- if∀: If decision makeris more important than decision maker, then the value ofis closer to the value derived by decision makerthan to that by decision maker.
3.3. Assessing the Suitability of a Smart Health Practice Using FWM
4. Application
- (1)
- C1: unobtrusiveness,
- (2)
- C2: supporting online social networking,
- (3)
- C3: cost effectiveness,
- (4)
- C4: availability of mobile health care facilities, and
- (5)
- C5: correct, reliable, and robust identification of a user’s need and situation.
- Very poor: (0, 0, 1),
- Poor: (0, 1, 2),
- Moderate: (1.5, 2.5, 3.5),
- Good: (3, 4, 5), and
- Very good: (4, 5, 5).
- (1)
- Smart mobile services were the most suitable smart health practice, while smart clothes were still the least suitable smart health practice, owing to their obtrusiveness.
- (2)
- The suitabilities of the eleven smart health practices were ranked. The results are shown in Figure 15. The ranking results of the sustainabilities of these smart health practices, retrieved from Chen [6], are also presented in this figure for comparison. Obviously, there are some differences between the two results. For example, the suitability of smart body analyzers was low, but its suitability was high, showing the great potential of smart body analyzers in the future.
- (3)
- In the experiment, decision makers modified their fuzzy judgment results just once to achieve a higher consensus, yet this was not always the case since modifications were subjectively made. It was possible for decision makers to undergo many rounds of collaboration before achieving a higher consensus. To tackle this problem, a mechanism for facilitating the collaboration process among decision makers should be designed.
- (4)
- The efficiency of ACO was a problem to the application of the fuzzy collaborative approach, and needed to be enhanced somehow, e.g., by applying a genetic algorithm. In previous studies, there were two major ways of combining genetic algorithms with fuzzy analytic hierarchy analysis. The first way obtains the weights of criteria by using fuzzy analytic hierarchy analysis, which designs the fitness function of the genetic algorithm to compare various alternatives. The second way solves a multi-objective optimization problem with a genetic algorithm to obtain multiple Pareto-optimal solutions, and then performs a fuzzy analytic hierarchy analysis to set the weights of the objective functions to further compare these Pareto-optimal solutions. The motive for applying a genetic algorithm in this study is different from those in previous studies.
- (5)
- Three existing methods, fuzzy ordered weighted average (FOWA), fuzzy geometric mean (FGM)-FWM, and the fuzzy extent analysis (FEA)-weighted average (WA) method proposed by Chang [29], were also applied to assess the suitability of each smart health practice for comparison. In FOWA, the moderately optimistic strategy was adopted. In FGM-FWM, fuzzy weights were approximated using FGM and expressed in terms of TFNs. In FEA-WA, since the weights estimated using FEA were crisp, WA, rather than FWM, was applied to assess the suitability of each smart health practice. Finally, the suitabilities of all smart health practices were ranked. The ranking results using various methods are compared in Figure 16. In sum, these methods came to the same conclusions about the suitabilities of smart mobile services and smart phones. In contrast, the suitabilities of other smart health practices assessed using different methods were not the same.
5. Conclusions
- (1)
- Smart mobile services and smart clothes were evaluated as the most suitable and the least suitable smart health practices, respectively.
- (2)
- The suitabilities of smart mobile services and smart phones evaluated using various methods were identical. In contrast, the suitabilities of other smart health practices, assessed using various methods, differed.
- (3)
- Among the compared methods, only the fuzzy collaborative approach could guarantee the existence of a consensus among decision makers. In other words, only the results assessed using the fuzzy collaborative approach were acceptable to all decision makers.
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Method | Smart Technology | Assessment Method | Group Decision Making | Consensus | Aggregation |
---|---|---|---|---|---|
Chen and Chiu [3] | All | Literature review | No | - | - |
Haymes et al. [4] | Not specified | Behavior analysis | No | - | - |
Chiu and Chen [5] | Smart mobile services | Learning curve analysis | No | - | - |
Chen [6] | All | FGM-ACO-FWM | Yes | Not guaranteed | Anterior-aggregation |
Chen [7] | All | FGM-FI-FWM | Yes | Guaranteed | Posterior-aggregation |
Our proposed methodology | All | ACO-FI-FWM | Yes | Guaranteed | Posterior-aggregation |
C1 | C2 | C3 | C4 | C5 | |
---|---|---|---|---|---|
C1 | 1 | (1, 5, 9), (3, 7, 9), (3, 7, 9) | (1, 1, 5), (1, 5, 9), (1, 1, 5) | (3, 7, 9), (5, 9, 9), (1, 1, 5) | (1, 5, 9), (1, 5, 9), (1, 3, 7) |
C2 | - | 1 | (1, 1, 5), (1, 4, 8), (1, 3, 7) | (3, 7, 9), (1, 3, 7), (1, 1, 5) | - |
C3 | - | - | 1 | - | (1, 5, 9), (1, 4, 8), (1, 5, 9) |
C4 | - | - | (1, 1, 5), (1, 3, 7), (1, 1, 5) | 1 | (3, 7, 9), (1, 5, 9), (1, 3, 7) |
C5 | - | (1, 3, 7), (1, 5, 9), (1, 4, 8) | - | - | 1 |
C1 | C2 | C3 | C4 | C5 | |
---|---|---|---|---|---|
C1 | 1 | (1, 5, 9), (3, 7, 9), (3, 7, 9) | (1, 1, 5), (1, 3, 7), (1, 1, 5) | (3, 7, 9), (1, 5, 9), (1, 1, 5) | (1, 5, 9), (1, 5, 9), (1, 3, 7) |
C2 | - | 1 | (1, 1, 5), (1, 1, 5), (1, 3, 7) | (3, 7, 9), (1, 5, 9), (1, 1, 5) | - |
C3 | - | - | 1 | - | (1, 5, 9), (1, 4, 8), (1, 5, 9) |
C4 | - | - | (1, 1, 5), (1, 3, 7), (1, 1, 5) | 1 | (3, 7, 9), (1, 4, 8), (1, 3, 7) |
C5 | - | (1, 3, 7), (1, 5, 9), (1, 4, 8) | - | - | 1 |
Smart Health Practice | C1 (Unobtrusiveness) | C2 (Online Social Networking) | C3 (Cost Effectiveness) | C4 (Availability of Mobile Health Care Facilities) | C5 (Correct, Reliable, and Robust Identification) |
---|---|---|---|---|---|
Smart body analyzers | (1.00, 2.00, 3.00) | (1.00, 2.00, 3.00) | (0.00, 1.00, 2.00) | (0.00, 1.00, 2.00) | (2.50, 3.50, 4.50) |
Smart clothes | (0.00, 1.00, 2.00) | (0.00, 1.00, 2.00) | (0.00, 1.00, 2.00) | (0.00, 1.00, 2.00) | (2.50, 3.50, 4.50) |
Smart glasses | (0.00, 1.00, 2.00) | (3.33, 4.33, 5.00) | (0.00, 1.00, 2.00) | (2.00, 3.00, 4.00) | (2.50, 3.50, 4.50) |
Smart mobile services | (3.67, 4.67, 5.00) | (3.67, 4.67, 5.00) | (3.67, 4.67, 5.00) | (3.67, 4.67, 5.00) | (3.33, 4.33, 5.00) |
Smart motion sensors | (2.00, 3.00, 4.00) | (0.00, 1.00, 2.00) | (0.00, 1.00, 2.00) | (0.00, 1.00, 2.00) | (3.67, 4.67, 5.00) |
Smart phones | (3.67, 4.67, 5.00) | (3.67, 4.67, 5.00) | (2.50, 3.50, 4.50) | (3.67, 4.67, 5.00) | (3.67, 4.67, 5.00) |
Smart smoke alarms | (2.00, 3.00, 4.00) | (0.00, 1.00, 2.00) | (1.00, 2.00, 3.00) | (2.00, 3.00, 4.00) | (3.67, 4.67, 5.00) |
Smart toilets | (1.00, 2.00, 3.00) | (0.00, 1.00, 2.00) | (0.00, 1.00, 2.00) | (0.00, 1.00, 2.00) | (2.00, 3.00, 4.00) |
Smart watches | (3.67, 4.67, 5.00) | (2.50, 3.50, 4.50) | (1.00, 2.00, 3.00) | (3.67, 4.67, 5.00) | (3.67, 4.67, 5.00) |
Smart wheelchairs | (2.50, 3.50, 4.50) | (0.00, 1.00, 2.00) | (0.00, 1.00, 2.00) | (2.00, 3.00, 4.00) | (2.00, 3.00, 4.00) |
Smart wigs | (2.00, 3.00, 4.00) | (0.00, 1.00, 2.00) | (0.00, 1.00, 2.00) | (0.00, 1.00, 2.00) | (2.50, 3.50, 4.50) |
Smart Health Practice | Defuzzified Suitability |
---|---|
Smart body analyzers | 2.10 |
Smart clothes | 1.52 |
Smart glasses | 2.40 |
Smart mobile services | 4.64 |
Smart motion sensors | 2.45 |
Smart phones | 4.54 |
Smart smoke alarms | 2.91 |
Smart toilets | 1.86 |
Smart watches | 4.17 |
Smart wheelchairs | 2.79 |
Smart wigs | 2.33 |
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Chen, T.-C.T.; Wang, Y.-C.; Lin, Y.-C.; Wu, H.-C.; Lin, H.-F. A Fuzzy Collaborative Approach for Evaluating the Suitability of a Smart Health Practice. Mathematics 2019, 7, 1180. https://doi.org/10.3390/math7121180
Chen T-CT, Wang Y-C, Lin Y-C, Wu H-C, Lin H-F. A Fuzzy Collaborative Approach for Evaluating the Suitability of a Smart Health Practice. Mathematics. 2019; 7(12):1180. https://doi.org/10.3390/math7121180
Chicago/Turabian StyleChen, Tin-Chih Toly, Yu-Cheng Wang, Yu-Cheng Lin, Hsin-Chieh Wu, and Hai-Fen Lin. 2019. "A Fuzzy Collaborative Approach for Evaluating the Suitability of a Smart Health Practice" Mathematics 7, no. 12: 1180. https://doi.org/10.3390/math7121180
APA StyleChen, T.-C. T., Wang, Y.-C., Lin, Y.-C., Wu, H.-C., & Lin, H.-F. (2019). A Fuzzy Collaborative Approach for Evaluating the Suitability of a Smart Health Practice. Mathematics, 7(12), 1180. https://doi.org/10.3390/math7121180