## 6 Assessment of the Platform
### Evaluating the Feature Satisfaction Index
(FS)** \\ \hline \multicolumn{5}{c}{**Functional Components**} \\ \hline Patient Profile Manager & 0.01 & 1 & 0.01 & 100\% \\ Symptom Manager & 0.01 & 1 & 0.01 & 100\% \\ Medical Image Manager & 0.03 & 1 & 0.03 & 100\% \\ Feedback Manager & 0.01 & 1 & 0.01 & 100\% \\ Diagnosis Session Manager & 0.015 & 0.8 & 0.012 & 80\% \\ Diagnosis Report Generator & 0.015 & 0.9 & 0.0135 & 90\% \\ Organ Segmentation Manager & 0.03 & 1 & 0.03 & 100\% \\ Lesion Segmentation Manager & 0.03 & 1 & 0.03 & 100\% \\ Tumor Segmentation Manager & 0.03 & 1 & 0.03 & 100\% \\ Medical Feature Analyzer & 0.03 & 0.7 & 0.021 & 70\% \\ Disease Type Classifier & 0.02 & 0.9 & 0.018 & 90\% \\ Disease Stage Classifier & 0.02 & 0.9 & 0.018 & 90\% \\ \multicolumn{5}{c}{**Sub-total**} \\ \hline \multicolumn{5}{c}{**Persistent Object Classes**} \\ \hline Patient & 0.01 & 1 & 0.01 & 100\% \\ Diagnosis Report & 0.015 & 1 & 0.015 & 100\% \\ CECT & 0.01 & 1 & 0.01 & 100\% \\ Diagnosis Session & 0.025 & 0.8 & 0.02 & 80\% \\ Disease & 0.025 & 1 & 0.025 & 100\% \\ Lesion & 0.025 & 1 & 0.025 & 100\% \\ Tumor & 0.025 & 1 & 0.025 & 100\% \\ Medical Feature & 0.025 & 0.7 & 0.0175 & 70\% \\ Organ Segmentation Model & 0.01 & 1 & 0.01 & 100\% \\ Lesion Segmentation Model & 0.01 & 1 & 0.01 & 100\% \\ Tumor Segmentation Model & 0.01 & 1 & 0.01 & 100\% \\ Disease Classification Model & 0.01 & 1 & 0.01 & 100\% \\ Segmentation Model & 0.01 & 1 & 0.01 & 100\% \\ Machine Learning (ML) Model & 0.02 & 0.9 & 0.018 & 90\% \\ Classification Model & 0.01 & 1 & 0.01 & 100\% \\ \multicolumn{5}{c}{**Sub-total**} \\ \hline \multicolumn{5}{c}{**Tasks in Diagnosis Process**} \\ \multicolumn{5}{c}{Step 3. Segment target organ} & 0.035 & 1 & 0.035 & 100\% \\ Step 4. Segment lesions & 0.035 & 1 & 0.035 & 100\% \\ Step 5. Segment tumors & 0.035 & 0.8 & 0.028 & 80\% \\ Step 6. Analyze medical features & 0.035 & 0.7 & 0.0245 & 70\% \\ Step 7. Analyze temporal changes & 0.035 & 0.8 & 0.028 & 80\% \\ Step 8. Classify disease occurrence & 0.03 & 0.8 & 0.024 & 80\% \\ Step 9. Classify disease stages & 0.03 & 0.9 & 0.027 & 90\% \\ Step 10. Generate diagnosis report & 0.015 & 1 & 0.015 & 100\% \\ \multicolumn{5}{c}{**Sub-total**} \\ \hline \multicolumn{5}{c}{**ML Models used in Diagnosis**} \\ \hline Organ Segmentation Model & 0.04 & 1 & 0.04 & 100\% \\ Lesion Segmentation Model & 0.05 & 1 & 0.05 & 100\% \\ Tumor Segmentation Model & 0.05 & 1 & 0.05 & 100\% \\ Image Feature Classifier & 0.05 & 0.7 & 0.035 & 70\% \\ Disease Type Classifier & 0.03 & 0.9 & 0.027 & 90\% \\ Disease Stage Classifier & 0.03 & 0.9 & 0.027 & 90\% \\ \hline \multicolumn{5}{c}{**Sub-total**} \\ \hline \multicolumn{5}{c}{**SRAND TOTAL**} \\ \hline \multicolumn{5}{c}{**2004**} \\ \hline \multicolumn{5}{c}{**2005**} \\ \hline \multicolumn{5}{c}{**2006**} \\ \hline \multicolumn{5}{c}{**2007**} \\ \hline \multicolumn{5}{c}{**3008**} \\ \hline \multicolumn{5}{c}{**4009**} \\ \hline \multicolumn{5}{c}{**4009**} \\ \hline \multicolumn{5}{c}{**5009**} \\ \hline \multicolumn{5}{c}{**5009**} \\ \hline \multicolumn{5}{c}{**6009**} \\ \hline \multicolumn{5}{c}{**609**} \\ \hline \multicolumn{5}{c}{**700**} \\ \hline \multicolumn{5}{c}{**800**} \\ \hline \multicolumn{5}{c}{**800**} \\ \hline \multicolumn{5}{c}{**900**} \\ \hline \multicolumn{5}{c}{**900**} \\ \hline \multicolumn{5}{c}{**100**} \\ \hline \multicolumn{5}{cEach category of the features is given an equal overall weight of 0.25, i.e., 25%. The weights of the 42 features are defined by the authors and developers who implement the platform. Unlike end-users, who typically assess feature satisfaction of application systems, the platform's features--comprising approaches, design paradigms, software methods, and ML model strategies--are best understood and evaluated by developers with technical expertise. For example, technical aspects such as the applicability, reusability, and customizability of components like the Medical Feature Analyzer, Diagnosis Session data component, and core ML models require in-depth knowledge of software design, which end-users may lack.