CHAPTER 4: RESULTS AND DISCUSSION
4.8 Validation and Evaluation the results
4.8.1 Sensitivity analysis results
4.8.2.1 The systematic ranking results for the LM 131
This section presents the systematic ranking assessment results of the three hospitals. Where the final rankings of the prioritised patients within each hospital are divided into three groups. All groups are distributed in a systematic manner, since the start of the 2nd group's ranking results
coincides with the end of the 1st group's ranking results, and the same holds true for the remaining groups. For the three hospitals, the 1st and 2nd groups include 5 patients, and 3rd group include 6 patients. The results of this assessment for patient’s prioritisation at each hospital are shown in Table 4.8.
Table 4. 8 Validation of prioritisation results of the eligible treatment patients at the LM Hospital 1
Groups Patients Mean
1st Group H1_P6, H1_P15, H1_P14, H1_P12, H1_P10 0.917
2nd Group H1_P2, H1_P16, H1_P3, H1_P7, H1_P11 0.897
3rd Group H1_P5, H1_P8, H1_P9, H1_P13, H1_P4, H1_P1 0.886 Hospital 2
Groups Patients Mean
1st Group H2_P9, H2_P4, H2_P14, H2_P8, H2_P3 0.921
2nd Group H2_P5, H2_P10, H2_P13, H2_P16, H2_P2 0.905
3rd Group H2_P12, H2_P1, H2_P7, H2_P6, H2_P15, H2_P11 0.878 Hospital 3
Groups Patients Mean
1st Group H3_P9, H3_P6, H3_P14, H3_P5, H3_P10 0.927
2nd Group H3_P12, H3_P8, H3_P15, H3_P13, H3_P3 0.908
3rd Group H3_P2, H3_P16, H3_P1, H3_P7, H3_P4, H3_P11 0.877
In Table 4.8 the assessment results are shown for each group per hospital. According to the comparisons, the 1st group obtained the highest mean, which is the best across all hospitals in terms of ranking results. Thus, the 1st group has the most eligible patients among all hospitals in terms of rankings. The findings show that the F-TOPSIS method used to prioritise patients follows a systematic ranking.
4.8.2.2 The systematic ranking results for the CFS
Same objective assessment has been performed to evaluate the first seven ranking results of the eligible treatment patients at the CFS. The ranking results of the prioritised patients are split into three groups. The 1st and 2nd groups each have two patients, whereas the 3rd group contains three patients. Table 4.9 presents the assessment results for patient’s prioritisation at CFS.
Table 4. 9 Validation of prioritisation results of the eligible treatment patients at the CFS
Group Patients Mean
1st Group H2_P9, H3_P9 0.947
2nd Group H2_P4, H3_P6 0.920
3rd Group H3_P14, H1_P6, H1_P15 0.916
In comparison to the second and third groups, the 1st group has the best and greatest mean value (0.947) as shown in Table 4.9. Meanwhile, the 2nd group's mean (0.920) is greater than the 3rd group's (0.916). This finding reveals that the prioritisation results of eligible treatment patients are robustness and consistent.
4.8.3 The results of Comparison analysis assessment
According to Section 3.3.3, the procedure of comparison analysis is applied and the results is presented in this section. The (FDMD) methodology is compared to the existing (MCDM) method (T. J. Mohammed et al., 2021). The two main challenges of distributing treatment for SARS-CoV- 2 patients over hospital networks are explored. These two main challenges were used as a checklist for comparing achievement in two aspects. First the application aspect in the medical field, more specific the available method or procedure in handling SARS-COV-2 treatment distribution issues
over the hospital networking in compared to the proposed one (FDMD), and second the theoretical aspects, in term of the privacy and prioritisation challenges as presented in Table 4.10.
Table 4. 10 Comparison analysis between proposed FDMD and Previous Study
Methodologies
Benchmarking check list
% Privacy
challenge Prioritisation Challenge
Data Share Issue Independence Issue
Weighting issue Ranking issue Weight
ing Process
inconsisten cy
Vaguen ess
Revers al rank
Multi - criter
ia Issue
Data Variati
on Issue
FDMD 100%
MCDM (Available) (T.
J. Mohammed et al., 2021)
37.5%
Table (4.10) demonstrated the achievement percentage of the discussed benchmark checklist, which clearly depict the superiority of FDMD (i.e. 8/8; 100% achievement) over the available MCDM method (i.e. 3/8; 37.5% achievement) (T. J. Mohammed et al. (2021).
From Application aspect in the medical field, although the available method proposed a protocol for the transfusion of efficient convalescent plasma (CP) distribution as a pre-vaccination treatment. Yet, this solution wasn’t good enough to save patients' life or reduce the speed of SARS- COV-2 infection transmission. However, it was the best available solution for the pre-vaccination period. In contrast, this study through the proposed FDMD focused on the distribution of anti-
SARS-CoV-2 mAbs for eligible patients as a recommended medicine for curing the SARS-COV- 2 patient in mild or moderate infection level.
In the theoretical aspect, from one side, the proposed FDMD overcome privacy challenge, that
ignored totally by the available method (T. J. Mohammed et al., 2021). The integration of federated fundamental in the proposed FDMD kept the patients' data private and not shared over the network during the computation process, in addition, each hospital can independently process their data patients which in return gives equal opportunity for all hospitals in receiving the anti-SARS-CoV- 2 mAbs for their own patients without overriding an individual hospital data over others. From the other side, FDMD, handled the prioritisation challenge more efficiently and effectively in compared to the available method (T. J. Mohammed et al., 2021).
The (T. J. Mohammed et al., 2021), used an integration framework between AHP as weighting method to compute the importance of the criteria and classical TOPSIS as ranking method to order the alternatives during the process of prioritisation and matching between the donors and patients to overcome the multicriteria and data variation issues. Although these two methods are considered as well-known methods in the context of MCDM in handling these issues, AHP and classical TOPSIS had been theoretically criticised because of the inconsistency issue in the AHP method, which increases dramatically when the number of criteria exceeds nine (Pamučar et al., 2018) and the rank reversal issue of the classical TOPSIS (Tang & Fang, 2018), which commonly occurs as a consequence of adding or removing alternative (i.e. admit or discharge patient). By contrast, the proposed FDMD introduced IVSH2-FWZIC as a new formulated weighting method which computes the importance of weight criteria with zero inconsistency and provides high-accuracy outcomes due to the adoption of IVSH2 fuzzy environment that override vagueness and ambiguity.
Finally, the proposed F-TOPSIS that was used in the sequencing process of FDMD succeed not
only in handling the multicriteria and data variation issues and employing the federation fundamental, but it solves the reversal ranking in an efficient way. Overall, in the basis of above comparison scenario with previous work proved the effectiveness of the proposed FDMD.