# Machine Learning for Evaluating Hospital Mobility: An Italian Case Study
## 4 Experimental Results and Discussions
### Impact of Machine Learning on Hospital Mobility: Perspectives and Challenges  
The use of logistic regression and SHAP values to analyze variables influencing hospital mobility opens new perspectives for understanding patients' perceptions of healthcare service quality. These techniques, augmented by advanced machine learning methods, significantly improve the transparency and interpretability of predictive models, allowing for the elucidation of complex variable relationships. Recent studies on seismic vulnerability and behavior in strategy games have validated the effectiveness of SHAP values in delivering detailed predictive insights across various domains [43; 44], highlighting how specific factors influence patient decisions about hospital mobility and providing valuable insights for optimizing healthcare services.  
Comparing two regions with distinct healthcare contexts has enriched this analysis, demonstrating how regional nuances can affect service quality perception. This approach is supported by studies investigating both perceived and technical healthcare quality in primary care settings, which have major implications for the sustainability of national health insurance schemes as evidenced in Ghana [45]. Additionally, an analysis between the Lombardy Region and national Italian data revealed substantial differences in hospital care quality and clinical outcomes, emphasizing the importance of regional context in healthcare quality assessments [46]. These examples underscore the critical role of regional comparisons in understanding and improving healthcare quality.  
Despite its significant contributions, this study has limitations, including its geographical scope confined to Apulia and Emilia-Romagna. Expanding the analysis to other regions or comparing Italian data with that of other countries could provide a more comprehensive perspective. Research has shown that interregional healthcare mobility within a decentralized healthcare system is influenced by factors such as regional income, hospital capacity, organizational structure, performance, and technology [47]. These factors are crucial in guiding patient healthcare choices outside their home region, offering insights for more effective health policies [48].  
Moreover, access to and the quality of data are critical aspects that can affect the generalizability of results. Future research should aim for a broader and more diversified data collection to address these limitations and further strengthen the research foundation. This study marks a significant step towards using machine learning to better analyze and understand hospital mobility and healthcare service quality perceptions.  
Future research should aim to expand the geographical scope and data availability, incorporating interdisciplinary perspectives for a more holistic understanding of hospital mobility dynamics. These insights emphasize the need for a deeper understanding and targeted strategies to address the challenges posed by interregional healthcare mobility, ensuring equity and efficiency in access to care across the national territory.