# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 5 Theoretical Evaluation  
The evaluation of the AI-powered stroke diagnosis system follows a structured framework designed to assess the performance of both the numeric clinical model and the image-based facial stroke detection model. This framework ensures that the models are rigorously tested for accuracy, reliability, and robustness. The evaluation consists of three main aspects: performance metrics, validation techniques, and comparative benchmarking.  
The model performance is measured using standard classification metrics, including the accuracy, precision, recall, and F1-score. These metrics are critical for determining how well the models differentiate between stroke and non-stroke cases. The system's effectiveness is also compared against baseline models, including traditional stroke screening methods such as human clinical evaluation and non-AI-based facial asymmetry assessments.  
To ensure model reliability, multiple validation techniques are applied. Cross-validation is used to divide the dataset into training, validation, and test sets, ensuring that the models generalize well to new data. Confusion matrix analysis is performed to identify patterns of misclassification, particularly false positives and false negatives. Techniques such as dropout layers and early stopping are implemented to mitigate overfitting and improve model robustness.  
Comparative benchmarking is conducted to evaluate the AI system's performance against existing stroke diagnosis methods. By comparing the accuracy of the AI model to traditional clinical assessments and non-AI facial recognition techniques, this study determines whether the proposed system offers a significant improvement in stroke detection.  
To further validate the model's generalizability, stratified cross-validation is employed, ensuring that each subset maintains a representative distribution of stroke vs. non-stroke cases. The model is also evaluated on an external test dataset that has not been seen during training. Additionally, ablation studies are conducted to assess the impact of different preprocessing techniques and architectural choices on the model performance.  
This chapter precisely outlines a detailed examination of the advanced framework we have created for stroke diagnosis, driven by artificial intelligence and utilizing a substantial dataset from the eICU Collaborative Research Database (eICU DB), complemented by carefully selected imaging sources that improve the integrity of our analysis. We undertake a detailed assessment of a variety of modeling methodologies, which encompass both conventional techniques such as gradient boosting exemplified by XGBoost and CatBoost, as well as ensemble strategies characterized by voting and stacking, in addition to a deep learning architecture founded on the MobileNet convolutional neural network, through the application of both quantitative performance metrics and qualitative visual diagnostic evaluations that provide a dual perspective on the model efficacy. Furthermore, we engage in a critical analysis of the outputs generated by these models, employing tools such as confusion matrices, receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration plots, probability distributions, and sensitivity analyses that collectively contribute to a nuanced understanding of the model performance. The evaluation process is further enriched and informed by the comprehensive review of model analysis logs, all of which serve to furnish deeper insights into the behavioral dynamics and limitations of the models, particularly within the challenging context of imbalanced clinical data that often complicate the diagnostic accuracy.