# Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare
## 5 Conclusions  
In this study, we provided a full pipeline for training and comparing survival ML and DL models, and we leveraged SurvSHAP to enrich our understanding of model predictions over time. In the context of survival analysis, this work aimed to address two crucial issues: (i) identifying the most reliable model for survival prediction among models performing equally well, and (ii) determining and analyzing the variable importance according to the observation time \(t\). We focused on the domain of obstructive sleep apnea, with a particular emphasis on the role of comorbidities. However, any other pathology may be considered as a use case for performing explainable survival analysis with the SurvSHAP method. In addition, we made comparisons among Machine Learning and Deep Learning survival models. The performance and reliability of the AI survival model in the OSA context are affected by several factors, such as data quality and quantity, features relevant to OSA pathology, and AI model architecture. Here, we demonstrated how complex survival models are particularly capable of identifying the features that most accurately describe the pathology under examination. After performing data exploration and preparation operations, we trained four survival ML models and five survival DL models. Then, we selected the best-performing models according to evaluation metrics computed on the test set. Finally, we leveraged SurvSHAP algorithms and classified the explanations into two categories--dataset- and model-level explanations--to investigate the differences among the best models based on this study's observation time. The achieved results illustrated the differences among the values of feature importance retrieved by ML and DL models, highlighting how the models identify the most important variables and how they affect the predictions over time. We demonstrated how time-dependent explainability, i.e., XAI(t), helps understand the model's behavior and the interpretation of data feature contributions to the survival function. Our XAI-based analysis showed that although the CPH model exhibited slightly higher performance, the LH model proved to be more reliable and clinically useful for supporting the follow-up of patients with OSA. Such a dynamic evaluation and understanding can enhance the clinical decision-making process during the follow-up stages of the rehabilitation process by assisting  
physicians in assigning different weights to the features related to patients' conditions based on their evolving importance over time. F.B. : Conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, writing--original draft, and writing--review and editing. P.M.M. : Conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, writing--original draft, and writing--review and editing. V.S. : Writing--original draft and writing--review and editing. S.C.: Visualization and writing--review and editing. G.P. : Conceptualization, resources, project administration, validation, and writing--review and editing. L.P.: Resources and data curation. M.A. : Resources, data curation, investigation, and validation. G.C. : Resources, data curation, investigation, and validation. P.G. : Resources. G.D.: Conceptualization, resources, and project administration. V.B. : Conceptualization, methodology, supervision, writing--review and editing, and funding acquisition. All authors have read and agreed to the published version of the manuscript. This study was funded under the research projects:  
* National Recovery and Resilience Plan (NRRP), Mission 4, Component 1, Investment 4.1, Decree No. 118 of the Italian Ministry of University and Research, Concession Decree No. 2333 of the Italian Ministry of University and Research, CUP D93C23000450005, within the Italian National Program Ph.D. Program in Autonomous Systems (DAuSy), co-funded by the European Union--Next-Generation EU.