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Year 2024, Volume: 37 Issue: 2, 1004 - 1020

Fulden Cantaş Türkiş İmran Kurt Omurlu Mevlüt Türe

https://doi.org/10.35378/gujs.1223015

Abstract

Keywords

Extreme learning machine Extreme learning machine Regularized Cox model Supervised Principal Components High Dimensional Survival Data Simulation

References

  • [1] Bair, E., and Tibshirani, R., “Semi-supervised methods to predict patient survival from gene expression data”, PLoS Biology, 2(4): 511-522, (2004).
  • [2] Verweij, P.J., and Van Houwelingen, H.C., “Penalized likelihood in Cox regression”, Statistics in Medicine, 13(23‐24): 2427-2436, (1994).
  • [3] Wang, H., and Li, G., “Extreme learning machine Cox model for high‐dimensional survival analysis”, Statistics in Medicine, 38(12): 2139-2156, (2019).
  • [4] Wang, H., Wang, J., and Zhou, L., “A survival ensemble of extreme learning machine”, Applied Intelligence, 48(7): 1846-1858, (2018).
  • [5] Delen, D., Walker, G., and Kadam, A., “Predicting breast cancer survivability: a comparison of three data mining methods”, Artificial Intelligence in Medicine, 34(2): 113-127, (2005).
  • [6] Dhillon, A., and Singh, A., “eBreCaP: extreme learning-based model for breast cancer survival prediction”, IET Systems Biology, 14(3): 160-169, (2020).
  • [7] Gittoa, S., Magistrib, P., Marzic, L., Mannellia, N., Mariab, N., Megac, A., Vitaled, G., Valentee, G., Vizzuttia, F., Villaf, E., Marraa, F., Andreoneg,h, P., Falcinia, M., Catellanib, B., Guerrinib, G.P., Serrab, V., Sandrob, S., Ballarinb, R., Piaie, G., Schepisf, F., Margottih, M., Cursaroh, C., Simonei, P., Petruccellii, S., Carraii, P., Fortej, P., Campanij, C., Zollerk, H., Benedettob, F., MEDITRA group, “Predictors of solid extra-hepatic non-skin cancer in liver transplant recipients and analysis of survival: a long-term follow-up study”, Annals of Hepatology, 27(3): 100683, (2022).
  • [8] Li, J., Zhou, Z., Dong, J., Fu, Y., Li, Y., Luan, Z., Peng, X., “Predicting breast cancer 5-year survival using machine learning: a systematic review”, PloS one, 16(4): e0250370, (2021).
  • [9] Lou, S.-J., Hou, M-F., Chang, H-T., Lee, H-H., Chiu, C-C., Jennifer Yeh, S-C., Shi, H-Y., “Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study”, Biology, 11(1): 47, (2022).
  • [10] Yu, Y., Xu, S., Zhaop, E., Dong, Y., Chen, J., Rao, B., Zeng, J., Yang, L., Lu, J., Qui, F., “Identification of a 10-pseudogenes signature as a novel prognosis biomarker for ovarian cancer”, Biocell, 46(4): 999, (2022).
  • [11] Wang, H., and Zhou, L., “SurvELM: an R package for high dimensional survival analysis with extreme learning machine”, Knowledge-Based Systems, 160: 28-33, (2018).
  • [12] Yang, H., Tian, J., Meng, B., Wang, K., Zheng, C., Liu, Y., Yan, J., Han, Q., Zhang, Y., “Application of Extreme Learning Machine in the Survival Analysis of Chronic Heart Failure Patients With High Percentage of Censored Survival Time”, Frontiers in cardiovascular medicine, 8: 726516, (2021).
  • [13] Preeti, Bala, R., and Singh, R.P., “A prediction survival model based on support vector machine and extreme learning machine for colorectal cancer”, in Advances in Information and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference (FICC), Springer, 2: 616-629, (2019).
  • [14] Wang, Y., Wang, H., Li, S., Wang, L., “Survival risk prediction of esophageal cancer based on the kohonen network clustering algorithm and kernel extreme learning machine”, Mathematics, 10(9): 1367, (2022).
  • [15] Sun, D., Li, A., Tang, B., Wang, M., “Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome”, Computer Methods and Programs in Biomedicine, 161: 45-53, (2018).
  • [16] Bair, E., Hastie, T., Paul, D., Tibshirani, R., “Prediction by supervised principal components”, Journal of the American Statistical Association, 101(473): 119-137, (2006).
  • [17] Türe, M., and Kurt Ömürlü, İ., “Development of a New Supervised Principal Component Analysis Based on Artificial Neural Networks in Gene Expression Data”, Osmangazi Tıp Dergisi, 40(1): 20-27, (2018).
  • [18] Aktürk Hayat, E., Türe, M., and Şenol, Ş., “An Alternative Dimension Reduction Approach to Supervised Principal Components Analysis in High Dimensional Survival Data”, Turkiye Klinikleri Journal of Biostatistics, 8(1): 21-29, (2016).
  • [19] Huang, G.-B., Zhu, Q-Y., and Siew, C-K., “Extreme learning machine: a new learning scheme of feedforward neural networks”, in 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), (2004).
  • [20] Huang, G.-B., “An insight into extreme learning machines: random neurons, random features and kernels”, Cognitive Computation, 6(3): 376-390, (2014).
  • [21] Huang, G.-B., Chen, L., and Siew, C.K., “Universal approximation using incremental constructive feedforward networks with random hidden nodes”, IEEE Trans. Neural Networks, 17(4): 879-892, (2006).
  • [22] Huang, G.-B., Zhu, Q-Y., and Siew, C-K., “Extreme learning machine: theory and applications”, Neurocomputing, 70(1-3): 489-501, (2006).
  • [23] Kasun, L.L.C., Yang, Y., Huang, G-B., Zhang, Z., “Dimension reduction with extreme learning machine”, IEEE transactions on Image Processing, 25(8): 3906-3918, (2016).
  • [24] Rumelhart, D.E., Hinton, G.E., and Williams, R.J., “Learning representations by back-propagating errors”, Nature, 323(6088): 533-536, (1986).
  • [25] Huang, G.-B., Zhou, H., Ding, X., Zhang, R., “Extreme learning machine for regression and multiclass classification”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2): 513-529, (2011).
  • [26] Deng, W.-Y., Ong, Y-S., and Zheng, Q-H., “A fast reduced kernel extreme learning machine”, Neural Networks, 76: 29-38, (2016).
  • [27] Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y., “Deep survival: A deep cox proportional hazards network”, Stat, 1050(2): 1-10, (2016).
  • [28] Benner, A., Zucknick, M., Hielscher, T., Ittrich, C., Mansmann, U., “High‐dimensional Cox models: the choice of penalty as part of the model building process”, Biometrical Journal, 52(1): 50-69, (2010).
  • [29] Kawaguchi, E.S., Suchard, M.A., Liu, Z., Li, G., “Scalable Sparse Cox's Regression for Large-Scale Survival Data via Broken Adaptive Ridge”, arXiv preprint arXiv:1712.00561, (2017).
  • [30] De Bin, R., “Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost”, Computational Statistics, 31(2): 513-531, (2016).
  • [31] Chen, X., Wang, L., Smith, J. D., Zhang, B., “Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes”, Bioinformatics, 24(21): 2474-2481, (2008).
  • [32] Gui, J., and Li, H., “Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data”, Bioinformatics, 21(13): 3001-3008, (2005).
  • [33] Graf, E., Schmoor, C., Sauerbrei, W., Schumacher, M., “Assessment and comparison of prognostic classification schemes for survival data”, Statistics in Medicine, 18(17‐18): 2529-2545, (1999).
  • [34] Schmid, M., Wright, M.N., and Ziegler, A., “On the use of Harrell’s C for clinical risk prediction via random survival forests”, Expert Systems with Applications, 63: 450-459, (2016).
  • [35] Chicco, D., and Jurman, G., “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation”, BMC Genomics, 21(1): 1-13, (2020).
  • [36] Cohen, J., “A coefficient of agreement for nominal scales”, Educational and Psychological Measurement, 20(1): 37-46, (1960).
  • [37] Davis, J., and Goadrich, M., “The relationship between Precision-Recall and ROC curves”, in Proceedings of the 23rd international conference on Machine learning, (2006).

Survival Prediction with Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation

Year 2024, Volume: 37 Issue: 2, 1004 - 1020

Fulden Cantaş Türkiş İmran Kurt Omurlu Mevlüt Türe

https://doi.org/10.35378/gujs.1223015

Abstract

Mortality risks of important diseases such as cancer can be estimated using gene profiles which are high-dimensional data obtained from gene expression sequences. However, it is impossible to analyze high-dimensional data with classical techniques due to multicollinearity, time-consuming processing load, and difficulty interpreting the results. For this purpose, extreme learning machine methods, which can solve regression and classification problems, have become one of the most preferred machine learning methods regarding fast data analysis and ease of application. The goal of this study is to compare estimation performance of risk score and short-term survival with survival extreme learning machine methods, L2-penalty Cox regression, and supervised principal components analysis in generated high-dimensional survival data. The survival models have been evaluated by Harrell’s concordance index, integrated Brier score, F1 score, kappa coefficient, the area under the curve, the area under precision-recall, accuracy, and Matthew’s correlation coefficient. Performances of risk score estimation and short-term survival prediction of the survival models for the censoring rates of 10%, 30%, 50% and 70% have been obtained in the range of 0.746-0.796, 0.739-0.798, 0.726-0.791, 0.708-0.784 for Harrell’s concordance index; 0.773-0.824, 0.772-0.824, 0.754-0.818, 0.739-0.808 for F1 score and 0.816-0.867, 0.808-0.865, 0.788-0.863, 0.776-0.851 for area under curve. All results showed that survival extreme learning machine methods that allow analyzing high-dimensional survival data without the necessity of dimension reduction perform very competitive with the other popular classical methods used in the study.

Keywords

Extreme learning machine Regularized Cox model Supervised principal components High-dimensional survival data simulation

References

  • [1] Bair, E., and Tibshirani, R., “Semi-supervised methods to predict patient survival from gene expression data”, PLoS Biology, 2(4): 511-522, (2004).
  • [2] Verweij, P.J., and Van Houwelingen, H.C., “Penalized likelihood in Cox regression”, Statistics in Medicine, 13(23‐24): 2427-2436, (1994).
  • [3] Wang, H., and Li, G., “Extreme learning machine Cox model for high‐dimensional survival analysis”, Statistics in Medicine, 38(12): 2139-2156, (2019).
  • [4] Wang, H., Wang, J., and Zhou, L., “A survival ensemble of extreme learning machine”, Applied Intelligence, 48(7): 1846-1858, (2018).
  • [5] Delen, D., Walker, G., and Kadam, A., “Predicting breast cancer survivability: a comparison of three data mining methods”, Artificial Intelligence in Medicine, 34(2): 113-127, (2005).
  • [6] Dhillon, A., and Singh, A., “eBreCaP: extreme learning-based model for breast cancer survival prediction”, IET Systems Biology, 14(3): 160-169, (2020).
  • [7] Gittoa, S., Magistrib, P., Marzic, L., Mannellia, N., Mariab, N., Megac, A., Vitaled, G., Valentee, G., Vizzuttia, F., Villaf, E., Marraa, F., Andreoneg,h, P., Falcinia, M., Catellanib, B., Guerrinib, G.P., Serrab, V., Sandrob, S., Ballarinb, R., Piaie, G., Schepisf, F., Margottih, M., Cursaroh, C., Simonei, P., Petruccellii, S., Carraii, P., Fortej, P., Campanij, C., Zollerk, H., Benedettob, F., MEDITRA group, “Predictors of solid extra-hepatic non-skin cancer in liver transplant recipients and analysis of survival: a long-term follow-up study”, Annals of Hepatology, 27(3): 100683, (2022).
  • [8] Li, J., Zhou, Z., Dong, J., Fu, Y., Li, Y., Luan, Z., Peng, X., “Predicting breast cancer 5-year survival using machine learning: a systematic review”, PloS one, 16(4): e0250370, (2021).
  • [9] Lou, S.-J., Hou, M-F., Chang, H-T., Lee, H-H., Chiu, C-C., Jennifer Yeh, S-C., Shi, H-Y., “Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study”, Biology, 11(1): 47, (2022).
  • [10] Yu, Y., Xu, S., Zhaop, E., Dong, Y., Chen, J., Rao, B., Zeng, J., Yang, L., Lu, J., Qui, F., “Identification of a 10-pseudogenes signature as a novel prognosis biomarker for ovarian cancer”, Biocell, 46(4): 999, (2022).
  • [11] Wang, H., and Zhou, L., “SurvELM: an R package for high dimensional survival analysis with extreme learning machine”, Knowledge-Based Systems, 160: 28-33, (2018).
  • [12] Yang, H., Tian, J., Meng, B., Wang, K., Zheng, C., Liu, Y., Yan, J., Han, Q., Zhang, Y., “Application of Extreme Learning Machine in the Survival Analysis of Chronic Heart Failure Patients With High Percentage of Censored Survival Time”, Frontiers in cardiovascular medicine, 8: 726516, (2021).
  • [13] Preeti, Bala, R., and Singh, R.P., “A prediction survival model based on support vector machine and extreme learning machine for colorectal cancer”, in Advances in Information and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference (FICC), Springer, 2: 616-629, (2019).
  • [14] Wang, Y., Wang, H., Li, S., Wang, L., “Survival risk prediction of esophageal cancer based on the kohonen network clustering algorithm and kernel extreme learning machine”, Mathematics, 10(9): 1367, (2022).
  • [15] Sun, D., Li, A., Tang, B., Wang, M., “Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome”, Computer Methods and Programs in Biomedicine, 161: 45-53, (2018).
  • [16] Bair, E., Hastie, T., Paul, D., Tibshirani, R., “Prediction by supervised principal components”, Journal of the American Statistical Association, 101(473): 119-137, (2006).
  • [17] Türe, M., and Kurt Ömürlü, İ., “Development of a New Supervised Principal Component Analysis Based on Artificial Neural Networks in Gene Expression Data”, Osmangazi Tıp Dergisi, 40(1): 20-27, (2018).
  • [18] Aktürk Hayat, E., Türe, M., and Şenol, Ş., “An Alternative Dimension Reduction Approach to Supervised Principal Components Analysis in High Dimensional Survival Data”, Turkiye Klinikleri Journal of Biostatistics, 8(1): 21-29, (2016).
  • [19] Huang, G.-B., Zhu, Q-Y., and Siew, C-K., “Extreme learning machine: a new learning scheme of feedforward neural networks”, in 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), (2004).
  • [20] Huang, G.-B., “An insight into extreme learning machines: random neurons, random features and kernels”, Cognitive Computation, 6(3): 376-390, (2014).
  • [21] Huang, G.-B., Chen, L., and Siew, C.K., “Universal approximation using incremental constructive feedforward networks with random hidden nodes”, IEEE Trans. Neural Networks, 17(4): 879-892, (2006).
  • [22] Huang, G.-B., Zhu, Q-Y., and Siew, C-K., “Extreme learning machine: theory and applications”, Neurocomputing, 70(1-3): 489-501, (2006).
  • [23] Kasun, L.L.C., Yang, Y., Huang, G-B., Zhang, Z., “Dimension reduction with extreme learning machine”, IEEE transactions on Image Processing, 25(8): 3906-3918, (2016).
  • [24] Rumelhart, D.E., Hinton, G.E., and Williams, R.J., “Learning representations by back-propagating errors”, Nature, 323(6088): 533-536, (1986).
  • [25] Huang, G.-B., Zhou, H., Ding, X., Zhang, R., “Extreme learning machine for regression and multiclass classification”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2): 513-529, (2011).
  • [26] Deng, W.-Y., Ong, Y-S., and Zheng, Q-H., “A fast reduced kernel extreme learning machine”, Neural Networks, 76: 29-38, (2016).
  • [27] Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y., “Deep survival: A deep cox proportional hazards network”, Stat, 1050(2): 1-10, (2016).
  • [28] Benner, A., Zucknick, M., Hielscher, T., Ittrich, C., Mansmann, U., “High‐dimensional Cox models: the choice of penalty as part of the model building process”, Biometrical Journal, 52(1): 50-69, (2010).
  • [29] Kawaguchi, E.S., Suchard, M.A., Liu, Z., Li, G., “Scalable Sparse Cox's Regression for Large-Scale Survival Data via Broken Adaptive Ridge”, arXiv preprint arXiv:1712.00561, (2017).
  • [30] De Bin, R., “Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost”, Computational Statistics, 31(2): 513-531, (2016).
  • [31] Chen, X., Wang, L., Smith, J. D., Zhang, B., “Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes”, Bioinformatics, 24(21): 2474-2481, (2008).
  • [32] Gui, J., and Li, H., “Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data”, Bioinformatics, 21(13): 3001-3008, (2005).
  • [33] Graf, E., Schmoor, C., Sauerbrei, W., Schumacher, M., “Assessment and comparison of prognostic classification schemes for survival data”, Statistics in Medicine, 18(17‐18): 2529-2545, (1999).
  • [34] Schmid, M., Wright, M.N., and Ziegler, A., “On the use of Harrell’s C for clinical risk prediction via random survival forests”, Expert Systems with Applications, 63: 450-459, (2016).
  • [35] Chicco, D., and Jurman, G., “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation”, BMC Genomics, 21(1): 1-13, (2020).
  • [36] Cohen, J., “A coefficient of agreement for nominal scales”, Educational and Psychological Measurement, 20(1): 37-46, (1960).
  • [37] Davis, J., and Goadrich, M., “The relationship between Precision-Recall and ROC curves”, in Proceedings of the 23rd international conference on Machine learning, (2006).
There are 37 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Statistics
Authors

Fulden Cantaş Türkiş 0000-0002-7018-7187

İmran Kurt Omurlu 0000-0003-2887-6656

Mevlüt Türe 0000-0003-3187-2322

Early Pub Date August 14, 2023
Publication Date
Published in Issue Year 2024 Volume: 37 Issue: 2

Cite

APA Cantaş Türkiş, F., Kurt Omurlu, İ., & Türe, M. (n.d.). Survival Prediction with Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation. Gazi University Journal of Science, 37(2), 1004-1020. https://doi.org/10.35378/gujs.1223015
AMA Cantaş Türkiş F, Kurt Omurlu İ, Türe M. Survival Prediction with Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation. Gazi University Journal of Science. 37(2):1004-1020. doi:10.35378/gujs.1223015
Chicago Cantaş Türkiş, Fulden, İmran Kurt Omurlu, and Mevlüt Türe. “Survival Prediction With Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation”. Gazi University Journal of Science 37, no. 2 n.d.: 1004-20. https://doi.org/10.35378/gujs.1223015.
EndNote Cantaş Türkiş F, Kurt Omurlu İ, Türe M Survival Prediction with Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation. Gazi University Journal of Science 37 2 1004–1020.
IEEE F. Cantaş Türkiş, İ. Kurt Omurlu, and M. Türe, “Survival Prediction with Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation”, Gazi University Journal of Science, vol. 37, no. 2, pp. 1004–1020, doi: 10.35378/gujs.1223015.
ISNAD Cantaş Türkiş, Fulden et al. “Survival Prediction With Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation”. Gazi University Journal of Science 37/2 (n.d.), 1004-1020. https://doi.org/10.35378/gujs.1223015.
JAMA Cantaş Türkiş F, Kurt Omurlu İ, Türe M. Survival Prediction with Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation. Gazi University Journal of Science.;37:1004–1020.
MLA Cantaş Türkiş, Fulden et al. “Survival Prediction With Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation”. Gazi University Journal of Science, vol. 37, no. 2, pp. 1004-20, doi:10.35378/gujs.1223015.
Vancouver Cantaş Türkiş F, Kurt Omurlu İ, Türe M. Survival Prediction with Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation. Gazi University Journal of Science. 37(2):1004-20.