# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 3 Methodological Framework
### Research Questions and Hypotheses  
Building upon the established theoretical foundation that serves as a cornerstone for our inquiry, our extensive study is propelled by a crafted series of research questions that are specifically intended to rigorously evaluate both the efficacy and the inherent limitations associated with various modeling methodologies utilized in the diagnosis of stroke. The initial research question delves into the effectiveness of gradient-boosting techniques, scrutinizing their predictive capabilities regarding stroke outcomes by utilizing structured clinical data derived from the eICU Database, which is recognized for its comprehensive nature. Our hypothesis posits that when these gradient-boosting models are bolstered by thorough feature engineering practices and normalization processes, they will demonstrate a commendable overall accuracy rate that surpasses the threshold of 92%; however, it is also anticipated that the sensitivity for accurately detecting stroke incidents may remain at a moderate level, a phenomenon that can be attributed to the prevalent issue of class imbalance within the dataset.  
The subsequent research question shifts its focus towards the performance metrics of deep learning models, with particular emphasis on the MobileNet-based Convolutional Neural Network, as we investigate its efficacy in analyzing carefully curated imaging data for the purpose of stroke diagnosis [51]. We put forth the hypothesis that this advanced CNN architecture will not only attain superior overall accuracy, likely around the impressive figure of 96%, but will also provide a significantly enhanced stroke recall rate, anticipated to exceed 90%, in comparison to models that rely exclusively on structured data inputs. This expectation is firmly grounded in the network's exceptional capability to extract intricate visual features, which are indispensable for making critical distinctions between stroke cases and non-stroke cases, thus highlighting its potential advantages in clinical settings.  
The third research question is dedicated to exploring the potential advantages offered by ensemble methods, specifically focusing on both soft-voting and stacking techniques, in their ability to enhance the predictive performance of our diagnostic system. We postulate that, while these ensemble strategies are likely to yield improvements in the overall accuracy and precision metrics, they may also introduce certain trade-offs, particularly concerning the recall rate for stroke detection. More specifically, we anticipate that the stacking ensemble approach could achieve the highest numerical accuracy, yet this may come at the expense of a diminished recall rate for the minority class, thereby necessitating a thoughtful and careful calibration of the decision thresholds employed in the diagnostic process.  
Ultimately, our research endeavors to investigate the ramifications of the probability calibration and the optimization of decision thresholds on clinical outcomes, with the hypothesis that fine-tuning these thresholds--guided by sensitivity analyses and the interpretation of calibration curves--will substantially enhance the equilibrium between the precision and recall metrics. Achieving this delicate balance is imperative for minimizing false-negative rates, which is particularly critical within the realm of stroke diagnosis, while also ensuring that acceptable levels of false positives are maintained throughout the diagnostic process.