DOI: 10.18521/ktd.1996338 ISSN: 1309-3878

Comparison of Machine Learning and Deep Learning-Based Survival Models: An Explainable Artificial Intelligence Approach

Esra Akaydın Gültürk, Emek Güldoğan
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Aim: To compare the predictive performance of ten survival models spanning statistical, tree-based, ensemble, kernel, and deep learning approaches on colon cancer survival data, and to interpret their predictions with explainable artificial intelligence methods.Material and Methods: The colon dataset from the R survival package was used (n=888; events=430; event rate 48.4%). Predictive performance was assessed by 5×3 nested cross-validation using Harrell's concordance index (C-index) and, for models with a native survival function, the integrated Brier score (IBS); time-dependent discrimination was examined with the cumulative/dynamic AUC. The stability of the point estimates was additionally summarised with 1000-iteration bootstrap 95% confidence intervals, which reflect apparent (within-sample) performance rather than generalisation. Explainability was addressed at three layers using SHAP, SurvSHAP(t), and SurvLIME.Results: In nested cross-validation the highest mean C-index values were very close across RSF (0.6600), XGBoost (0.6596), and DeepSurv (0.6578); RSF showed the most balanced performance, with the highest mean C-index, the lowest IBS (0.1740) and the highest time-dependent AUC (TD-AUC=0.7204). In the within-sample bootstrap ExtraSurvTrees and RSF attained the highest apparent C-index (0.7342 and 0.7307); however, these values are optimistically biased relative to the nested cross-validation estimates. SHAP identified the number of positive lymph nodes as the strongest prognostic factor, SurvSHAP(t) revealed time-dependent variation in feature importance, and SurvLIME produced patient-level interpretable risk profiles.Conclusion: Ensemble methods demonstrated consistent and stable cross-validated performance, whereas deep learning models, although competitive on average, carried a risk of instability on this small-to-medium dataset. The explainability analyses confirmed clinically interpretable prognostic patterns and may inform the future development of clinical decision-support tools, pending external validation.

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