# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 5 Theoretical Evaluation
### Comparative Analysis
Figure 5: Voting ensemble analysis: (**a**) Voting ensemble calibration curve—illustrates how the ensemble’s probability estimates compare with actual outcomes; (**b**) Voting ensemble probability distribution—shows the distribution of probabilities generated by the voting ensemble; (**c**) Voting ensemble sensitivity analysis—depicts the sensitivity of the voting ensemble to threshold variations. Figure 6: Stacking ensemble analysis: (**a**) Stacking ensemble calibration curve—assesses the calibration of the stacking ensemble’s output; (**b**) Stacking ensemble probability distribution—illustrates the skew in probability assignments, notably a heavy concentration of low probabilities; (**c**) Stacking ensemble sensitivity analysis—reveals a steep decline in stroke recall with even modest threshold increases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Aspect** & **XGBoost/CatBoost** & **Voting Ensemble** & **Stacking Ensemble** & **CNN (Images)** \\ \hline Overall Accuracy & High (\(\sim\)92–93\%) & Similar to base models (\(\sim\)92.8\%) & Highest among numeric models (94.4\%) & Highest overall (96.0\%) \\ Stroke Detection & Moderate (\(\sim\)47\%) & Moderate (\(\sim\)46\%) & Low (\(\sim\)26\%) & Excellent (\(\sim\)91\%) \\ Probability & Overlap in mid-range probabilities & Heavy concentration in low-probability region & Skewed heavily towards low probabilities & Bimodal distribution (clear separation) \\  & Underestimation at high probabilities & Under-confident & Reasonable in mid-range; overconfident at extremes & Closely aligned with ideal diagonal (Figure 6a) & ideal distribution (Figure 7a) \\ Sensitivity to & Gradual decline in recall with threshold increase & Steep decline beyond threshold \(\sim\)0.3–0.4 & Very steep decline; high precision at expense of recall (Figure 6c) & Stable sensitivity across a wide threshold range \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Qualitative model characteristics. To contextualize our findings, we compared them with current work on AI-driven stroke diagnostic systems utilizing medical imaging data. Table 4 encapsulates essential findings from current investigations employing deep learning models, such as ResNet, VGG, DenseNet, and hybrid ensembles. These studies indicate stroke detection accuracies between 89% and 95%, frequently necessitating high-resolution imaging, GPU resources,  
Figure 7: CNN-based image model analysis: (**a**) CNN calibration curve—demonstrates that the CNN’s predicted probabilities are closely aligned with true outcomes; (**b**) CNN probability distribution—shows a near-bimodal distribution, indicating clear differentiation between stroke and no-stroke cases; (**c**) CNN sensitivity analysis—confirms stable sensitivity across a wide range of thresholds; (**d**) CNN confusion matrix—shows the raw confusion matrix indicating the number of correctly and incorrectly classified cases; (**e**) Normalized CNN confusion matrix—illustrates the percentage distribution of classifications between stroke and no-stroke. or restricted datasets. Our MobileNet-V2-based model attains 96% accuracy and 91% recall utilizing a CPU-only training environment and pseudo-randomly allocated curated pictures. This comparison validates the robustness and generalizability of our system, demonstrating that lightweight architectures such as MobileNet-V2 may equal or surpass the performance of more substantial models when integrated with calibration and preprocessing techniques.