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

Esra Akaydın Gültürk; Emek Güldoğan

doi:10.18521/ktd.1996338

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

Abstract

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.

Keywords

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Details

Primary Language

English

Subjects

Health Informatics and Information Systems

Journal Section

Research Article

Authors

Esra Akaydın Gültürk ^*
0000-0003-0978-3091
Türkiye

Emek Güldoğan
0000-0002-5436-8164
Türkiye

Publication Date

October 27, 2026

Submission Date

July 16, 2026

Acceptance Date

October 20, 2026

Published in Issue

Year 2026 Volume: 18 Number: 3

DOI

https://doi.org/10.18521/ktd.1996338

IZ

https://izlik.org/JA73YM55NM

Cite

RIS / Bibtex

APA

Akaydın Gültürk, E., & Güldoğan, E. (2026). Comparison of Machine Learning and Deep Learning-Based Survival Models: An Explainable Artificial Intelligence Approach. Konuralp Medical Journal, 18(3), 317-331. https://doi.org/10.18521/ktd.1996338

AMA

1.Akaydın Gültürk E, Güldoğan E. Comparison of Machine Learning and Deep Learning-Based Survival Models: An Explainable Artificial Intelligence Approach. Konuralp Medical Journal. 2026;18(3):317-331. doi:10.18521/ktd.1996338

Chicago

Akaydın Gültürk, Esra, and Emek Güldoğan. 2026. “Comparison of Machine Learning and Deep Learning-Based Survival Models: An Explainable Artificial Intelligence Approach”. Konuralp Medical Journal 18 (3): 317-31. https://doi.org/10.18521/ktd.1996338.

EndNote

Akaydın Gültürk E, Güldoğan E (October 1, 2026) Comparison of Machine Learning and Deep Learning-Based Survival Models: An Explainable Artificial Intelligence Approach. Konuralp Medical Journal 18 3 317–331.

IEEE

[1]E. Akaydın Gültürk and E. Güldoğan, “Comparison of Machine Learning and Deep Learning-Based Survival Models: An Explainable Artificial Intelligence Approach”, Konuralp Medical Journal, vol. 18, no. 3, pp. 317–331, Oct. 2026, doi: 10.18521/ktd.1996338.

ISNAD

Akaydın Gültürk, Esra - Güldoğan, Emek. “Comparison of Machine Learning and Deep Learning-Based Survival Models: An Explainable Artificial Intelligence Approach”. Konuralp Medical Journal 18/3 (October 1, 2026): 317-331. https://doi.org/10.18521/ktd.1996338.

JAMA

1.Akaydın Gültürk E, Güldoğan E. Comparison of Machine Learning and Deep Learning-Based Survival Models: An Explainable Artificial Intelligence Approach. Konuralp Medical Journal. 2026;18:317–331.

MLA

Akaydın Gültürk, Esra, and Emek Güldoğan. “Comparison of Machine Learning and Deep Learning-Based Survival Models: An Explainable Artificial Intelligence Approach”. Konuralp Medical Journal, vol. 18, no. 3, Oct. 2026, pp. 317-31, doi:10.18521/ktd.1996338.

Vancouver

1.Esra Akaydın Gültürk, Emek Güldoğan. Comparison of Machine Learning and Deep Learning-Based Survival Models: An Explainable Artificial Intelligence Approach. Konuralp Medical Journal. 2026 Oct. 1;18(3):317-31. doi:10.18521/ktd.1996338