# Machine Learning for Evaluating Hospital Mobility: An Italian Case Study
## 4 Experimental Results and Discussions
### Experiments  
In our analysis on hospital mobility prediction, we employed advanced methodologies to identify the optimal predictive model. Our comparative assessment is summarized in Table 12, which includes the following models:  
1. Logistic regression: Known for its simplicity and efficiency in binary classification problems, it proved effective in handling linear relationships between the predictive features and the target variable [32]. 2. Random forest: This ensemble method, which builds multiple decision trees and merges them to obtain a more accurate and stable prediction, was particularly useful for its robustness against overfitting and its ability to handle nonlinear data [33]. 3. Gradient boosting: Another ensemble technique that optimizes predictive accuracy by combining multiple weak predictive models to form a strong predictor. It performed well in scenarios requiring the handling of various types of data irregularities [34]. 4. Support vector machine (SVM): Designed to find the hyperplane that best divides a dataset into classes, SVM was valuable for its effectiveness in high-dimensional spaces, particularly when the number of dimensions exceeds the number of samples [35]. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **W** & \(\boldsymbol{p}\) \\ \hline
**BASE LEVEL** & **FIRST LEVEL** & \(-1.63\) & 0.780 \\
**BASE LEVEL** & **IRCCS** & \(1.81\) & 0.703 \\
**BASE LEVEL** & **PRIVATE NURSING HOMES** & \(5.57\) & **<0.001** \\
**BASE LEVEL** & **SECOND LEVEL** & \(-4.33\) & **0.019** \\
**FIRST LEVEL** & **IRCCS** & \(2.05\) & 0.593 \\
**FIRST LEVEL** & **PRIVATE NURSING HOMES** & \(5.16\) & **0.002** \\
**FIRST LEVEL** & **SECOND LEVEL** & \(-2.38\) & 0.446 \\
**IRCCS** & **PRIVATE NURSING HOMES** & \(1.11\) & 0.936 \\
**IRCCS** & **SECOND LEVEL** & \(-3.35\) & 0.124 \\
**PRIVATE NURSING** & **SECOND LEVEL** & \(-7.35\) & **<0.001** \\
**HOMES** & **SECOND LEVEL** & \(-7.35\) & **<0.001** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Dwass-Steel-Critchlow–Figner pairwise comparisons. 5. Neural networks: Extremely versatile and powerful, this model can capture complex nonlinearities in data through layers of neurons that simulate human decision-making processes. Neural networks are particularly effective in large datasets and with variable complexity, adapting well to multiple application scenarios [36]. 6. k-nearest neighbors (kNN): A simple, instance-based learning algorithm where the function is only approximated locally and all computation is deferred until classification. The kNN approach was beneficial due to its interpretability and minimal assumption about the structure of the data [37]. 7. Naive Bayes: A group of simple probabilistic classifiers based on applying [PERSON] theorem with strong (naive) independence assumptions between the features. It is often remarkably efficient in large datasets [38]. 8. AdaBoost: A boosting algorithm that can be used in conjunction with many other types of learning algorithms to improve performance. The output of the other learning algorithms (\"weak learners\") is combined into a weighted sum that represents the final output of the boosted classifier [39]. We evaluated these models based on several key performance metrics such as area under the curve (AUC), accuracy, F1 score, precision, recall, and Matthews correlation coefficient (MCC). This selection of metrics allows for a comprehensive assessment of performance, guiding the choice of the most suitable model for hospital mobility prediction. One study examined the effectiveness of predictive models for the early diagnosis of diabetes, emphasizing the critical role of model selection in healthcare outcomes [40]. Another work discussed the development and deployment of predictive models in the healthcare sector, providing practical insights into predictive modeling in healthcare [41]. Furthermore, the comparison of predictive models for hospital readmission of heart failure patients was analyzed, highlighting the importance of cost considerations in model evaluation [42].