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Article

Machine Learning vs. Survival Analysis Models: a study on right censored heart failure data

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Pages 1899-1916 | Received 03 Sep 2021, Accepted 26 Mar 2022, Published online: 11 Apr 2022

References

  • Aalen, O, and H. Gjessing. 2001. Understanding the shape of the hazard rate: A process point of view. Statistical Science 16 (1):1–14. doi:10.1214/ss/998929473.
  • Ahmad, T., A. Munir, S. H. Bhatti, M. Aftab, and M. A. Raza. 2017. Survival analysis of heart failure patients: A case study. Plos One 12 (7):e0181001. doi:10.1371/journal.pone.0181001.
  • Antolini, L., B.-H. Nam, and R. D'Agostino. 2004. Inference on correlated discrimination measures in survival analysis: A nonparametric approach. Communications in Statistics – Theory and Methods 33 (9):2117–35. doi:10.1081/STA-200026579.
  • Baayen, R. H., S. Vasishth, D. Bates, and R. Kliegl. 2017. The cave of shadows. Addressing the human factor with generalized additive mixed models. Journal of Memory and Language 94:206–34. doi:10.1016/j.jml.2016.11.006.
  • Baesens, B., T. Van Gestel, M. Stepanova, D. Van den Poel, and J. Vanthienen. 2005. Neural network survival analysis for personal loan data. Journal of the Operational Research Society 56 (9):1089–98. doi:10.1057/palgrave.jors.2601990.
  • Basak, P., A. Linero, D. Sinha, and S. Lipsitz. 2021. Semiparametric analysis of clustered interval-censored survival data using soft Bayesian additive regression trees (SBART). Biometrics:1–14. doi:10.1111/biom.13478.
  • Biard, L., A. Bergeron, V. Lévy, and S. Chevret. 2021. Bayesian survival analysis for early detection of treatment effects in phase 3 clinical trials. Contemporary Clinical Trials Communications 21:100709. Jan 9doi:10.1016/j.conctc.2021.100709.
  • Breiman, L. 1996. Bagging predictors. Machine Learning 24 (2):123–40. doi:10.1007/BF00058655.
  • Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
  • Brook, R. J. 1982. On the loss of information through censoring. Biometrika 69 (1):137–44.
  • Chen, L. P, and G. Y. Yi. 2021. Semiparametric methods for left-truncated and right-censored survival data with covariate measurement error. Annals of the Institute of Statistical Mathematics 73 (3):481–517. doi:10.1007/s10463-020-00755-2.
  • Chicco, D, and G. Jurman. 2020. Machine Learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20 (1):16. doi:10.1186/s12911-020-1023-5.
  • Cox, D. R. 1972. Regression Models and Life-Tables. Journal of the Royal Statistical Society. Series B (Methodological), 34 (2):187–220.
  • David, C. R. 1972. Regression models and life tables. Journal of the Royal Statistical Society 34 (2):187–220.
  • Dobson, A. J. 2001. An introduction to generalized linear models. 2nd ed. London: Chapman and Hall/CRC.
  • Faraggi, D, and R. Simon. 1995. A neural network model for survival data. Statistics in Medicine 14 (1):73–82. doi:10.1002/sim.4780140108.
  • Farewell, V. T. 1982. The use of mixture models for the analysis of survival data with long-term survivors. Biometrics 38 (4):1041–6.
  • Fathi, M., M. Nemati, S. M. Mohammadi, and R. Abbasi-Kesbi. 2020. A machine learning approach based on SVM for classification of liver diseases. Biomedical Engineering: Applications, Basis and Communications 32 (03):2050018. doi:10.4015/S1016237220500180.
  • Felizzi, F., N. Paracha, J. Pöhlmann, and J. Ray. 2021. Mixture cure models in oncology: A tutorial and practical guidance. PharmacoEconomics – Open 5 (2):143–55. PMID: 33638063. doi:10.1007/s41669-021-00260-z.
  • Fotso, S. 2018. Deep neural networks for survival analysis based on a multi-task framework. arXiv:1801. 05512.
  • Goldberg, Y, and M. Kosorok. 2017. Support vector regression for right censored data. Electronic Journal of Statistics 11 (1):532–69. doi:10.1214/17-EJS1231.
  • Gonzalez Ginestet, P., A. Kotalik, D. M. Vock, J. Wolfson, and E. E. Gabriel. 2021. Stacked inverse probability of censoring weighted bagging: A case study in the InfCareHIV register. Journal of the Royal Statistical Society: Series C (Applied Statistics) 70 (1):51–65. doi:10.1111/rssc.12448.
  • Guyon, I, and A. Elisseeff. 2003. An introduction to variable and feature selection. Journal of ML Research Special Issue on Variable and Feature Selection 3:1157–82.
  • Hosten, A. O. 1990. In: Walker HK, Hall WD, Hurst JW, editors. Clinical methods: The history. Physical, and laboratory examinations. 3rd ed. Boston: Butterworths; Chapter 193.
  • Hothorn, T., B. Lausen, A. Benner, and M. Radespiel-Tröger. 2004. Bagging survival trees. Statistics in Medicine 23 (1):77–91. doi:10.1002/sim.1593.
  • Hung, H, and C.-T. Chiang. 2010. Optimal composite markers for time-dependent receiver operating characteristic curves with. Scandinavian Journal of Statistics 37 (4):664–79. doi:10.1111/j.1467-9469.2009.00683.x.
  • Ibrahim, A., B. Bennett, and F. Isiaka. 2015. The optimisation of Bayesian classifier in predictive spatial modelling for secondary mineral deposits. Procedia Computer Science 61:478–85. doi:10.1016/j.procs.2015.09.194.
  • Ishwaran, H., U. Kogalur, E. Blackstone, and M. Lauer. 2008. Random survival forests. The Annals of Applied Statistics 2 (3):841–60. doi:10.1214/08-AOAS169.
  • Jaeger, B., L. Long, D. Long, M. Sims, J. Szychowski, Y.-I. Min, L. Mcclure, G. Howard, and N. Simon. 2019. Oblique random survival forests. The Annals of Applied Statistics 13 (3):1847–83. doi:10.1214/19-AOAS1261.
  • Jahanbani Fard, M., P. Wang, S. Chawla, and C. Reddy. 2016. A Bayesian perspective on early stage event prediction in longitudinal data. IEEE Transactions on Knowledge and Data Engineering 28 (12):3126–39. doi:10.1109/TKDE.2016.2608347.
  • Kamarudin, A. N., T. Cox, and R. Kolamunnage-Dona. 2017. Time-dependent ROC curve analysis in medical research: Current methods and applications. BMC Medical Research Methodology 17 (1):53. doi:10.1186/s12874-017-0332-6.
  • Kaplan, E, and P. Meier. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53 (282):457–81. doi:10.1080/01621459.1958.10501452.
  • Khan, F. M., and Zubek, V. B. 2008, December. Support vector regression for censored data (SVRc): A novel tool for survival analysis. In 2008 Eighth IEEE International Conference on Data Mining, IEEE, 863–868.
  • Kuo, B, and D. Landgrebe. 2002. A covariance estimator for small sample size classification problems and its application to feature extraction. IEEE Trans. on Geoscience and Remote Sensing 40 (4):814–9. doi:10.1109/TGRS.2002.1006358.
  • Lai, C. D., M. Xie, and D. N. P. Murthy. 2003. A modified Weibull distribution, In IEEE Transactions on Reliability 52 (1):33–7. doi:10.1109/TR.2002.805788.
  • Lee, E. T, and O. T. Go. 1997. Survival analysis in public health research. Annual Review of Public Health 18:105–34. doi:10.1146/annurev.publhealth.18.1.105.
  • Leung, K.-M, R. Elashoff, and A. A. Afifi. 1997. Censoring issues in survival analysis. Annual Review of Public Health 18:83–104. doi:10.1146/annurev.publhealth.18.1.83.
  • Li, Y., T. Chen, and T. Chen. 2021. An interpretable machine learning survival model for predicting long-term kidney outcomes in IgA nephropathy. AMIA Annu Symp Proc:737–46.
  • Lindsey, J, and L. Ryan. 1998. Methods for interval-censored data. Statistics in Medicine 17 (2):219–38. doi:10.1002/(SICI)1097-0258(19980130)17:2<219::AID-SIM735>3.0.CO;2-O.
  • Lisboa, P. J. G., H. Wong, P. Harris, and R. Swindell. 2003. A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer. Artificial Intelligence in Medicine 28 (1):1, 1–25.
  • Mariani, L., D. Coradini, E. Biganzoli, P. Boracchi, E. Marubini, S. Pilotti, B. Salvadori, R. Silvestrini, U. Veronesi, R. Zucali, et al. 1997. Prognostic factors for metachronous contralateral breast cancer: A comparison of the linear Cox regression model and its artificial neural network extension. Breast Cancer Research and Treatment 44 (2):167–78. doi:10.1023/A:1005765403093.
  • McBean, E. A, and B. Snider. 2021. Combining machine learning and survival statistics to predict remaining service life of watermains. Journal of Infrastructure Systems 27 (3):04021019. doi:10.1061/(ASCE)IS.1943-555X.0000629.
  • Moncada-Torres, A., M. van Maaren, M. Hendriks, S. Siesling, and G. Geleijnse. 2021. Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Scientific Reports 11 (1):6968. doi:10.1038/s41598-021-86327-7.
  • Muthukrishnan, R, and R. Rohini. 2016. LASSO: A feature selection technique in predictive modeling for machine learning. 2016 IEEE International Conference on Advances in Computer Applications (ICACA), pp. 18–20. doi:10.1109/ICACA.2016.7887916.
  • Nemati, M., J. Ansary, and N. Nemati. 2020. Machine-learning approaches in COVID-19 SA and discharge-time likelihood prediction using clinical data. Patterns 1 (5):100074. doi:10.1016/j.patter.2020.100074.
  • Nti, I. K., A. F. Adekoya, and B. A. Weyori. 2020. A comprehensive evaluation of ensemble learning for stock-market prediction. Journal of Big Data 7 (1):20. doi:10.1186/s40537-020-00299-5.
  • Peterson, L. 2009. K-nearest neighbor. Scholarpedia 4 (2):1883. doi:10.4249/scholarpedia.1883.
  • Powell, J. 1994. Estimation of semiparametric models handbook of econometrics 4:2443–521.
  • Prado, E., R. a. Moral, and A. Parnell. 2021. Bayesian additive regression trees with model trees. Statistics and Computing 31 (3):1–13. doi:10.1007/s11222-021-09997-3.
  • Prinja, S., N. Gupta, and R. Verma. 2010. Censoring in clinical trials: Review of survival analysis techniques. Indian Journal of Community Medicine 35 (2):217–21. doi:10.4103/0970-0218.66859.
  • Ravindra, K., P. Rattan, S. Mor, and A. N. Aggarwal. 2019. Ashutosh Nath Aggarwal, generalized additive models: Building evidence of air pollution, climate change and human health. Environment International 132:104987. doi:10.1016/j.envint.2019.104987.
  • Roshani, D, and E. Ghaderi. 2016. Comparing smoothing techniques for fitting the nonlinear effect of covariate in cox models. Acta Informatica Medica: AIM: Journal of the Society for Medical Informatics of Bosnia & Herzegovina: Casopis Drustva za Medicinsku Informatiku BiH 24 (1):38–41. doi:10.5455/aim.2016.24.38-41.
  • Sachs, M. C., A. Discacciati, Å. H. Everhov, O. Olén, and E. E. Gabriel. 2019. Ensemble prediction of time-to-event outcomes with competing risks: A case-study of surgical complications in Crohn’s disease. Journal of the Royal Statistical Society: Series C (Applied Statistics) 68 (5):1431–46. doi:10.1111/rssc.12367.
  • Schapire, R. E., Y. Freund, P. Bartlett, and W. S. Lee. 2020. Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics 26:1651–86.
  • Schuster, N. A., E. O. Hoogendijk, A. A. L. Kok, J. W. R. Twisk, and M. W. Heymans. 2020. Ignoring competing events in the analysis of survival data may lead to biased results: A nonmathematical illustration of competing risk analysis. Journal of Clinical Epidemiology 122:42–8.
  • Shin, S., P. Austin, H. Ross, H. Abdel-Qadir, C. Freitas, G. Tomlinson, D. Chicco, M. Mahendiran, P. Lawler, F. Billia, et al. 2021. Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality. ESC Heart Failure 8 (1):106–15.
  • Singh, R, and K. Mukhopadhyay. 2011. Survival analysis in clinical trials: Basics and must know areas. Perspectives in Clinical Research 2 (4):145–8. doi:10.4103/2229-3485.86872.
  • Smola, A, and B. Schölkopf. 2004. A tutorial on support vector regression. Statistics and Computing 13 (2):199–222.
  • Spanbauer, C, and R. Sparapani. 2021. Nonparametric machine learning for precision medicine with longitudinal clinical trials and Bayesian additive regression trees with mixed models. Statistics in Medicine 40 (11):2665–91. doi:10.1002/sim.8924.
  • Spooner, A., E. Chen, A. Sowmya, P. Sachdev, N. Kochan, J. Trollor, and H. Brodaty. 2020. A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction. Scientific Reports 10 (1):20410. doi:10.1038/s41598-020-77220-w.
  • Štajduhar, I, and B. Dalbelo Bašić. 2012. Uncensoring censored data for machine learning: A likelihood-based approach. Expert Systems with Applications 39 (8):7226–34. doi:10.1016/j.eswa.2012.01.054.
  • Sun, C., H. Li, R. E. Mills, and Y. Guan. 2019. Prognostic model for multiple myeloma progression integrating gene expression and clinical features. GigaScience 8 (12):1–10. doi:10.1093/gigascience/giz153.
  • Taunk, K., S. De, S. Verma, and A. Swetapadma. 2019. A brief review of nearest neighbor algorithm for learning and classification. 2019 International Conference on Intelligent Computing and Control Systems (ICCS) 1255–1260. doi:10.1109/ICCS45141.2019.9065747.
  • Taylor, J. 1995. Semi-parametric estimation in failure time mixture models. Biometrics 51 (3):899–907. doi:10.2307/2532991.
  • Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58 (1):267–88. doi:10.1111/j.2517-6161.1996.tb02080.x.
  • Uno, H., T. Cai, L. Tian, and L. J. Wei. 2007. Evaluating prediction rules for t-year survivors with censored regression models. Journal of the American Statistical Association 102 (478):527–37. doi:10.1198/016214507000000149.
  • Vinzamuri, B., Y. Li, and C. Reddy. 2014. Active learning based survival regression for censored data. CIKM 2014 – Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. 241–50. [Mismatch
  • Wang, P., Y. Li, and C. K. Reddy. 2019. Machine learning for survival analysis: A survey. ACM Computing Surveys 51 (6):1–36. doi:10.1145/3214306.
  • Weston, J., A. Elisseeff, B. Schölkopf, and M. Tipping. 2003. Use of the zero-norm with linear models and Kernel methods. Journal of Machine Learning Research Special Issue on Variable and Feature Selection 3:1439–61.
  • Wood, S. 2017. Generalized additive models: An introduction with R. 2nd ed. New York: Chapman and Hall/CRC.
  • Yan, K. K., X. Wang, W. W. T. Lam, V. Vardhanabhuti, A. W. M. Lee, and H. H. Pang. 2020. Radiomics analysis using stability selection supervised component analysis for right-censored survival data 2021. Computers in Biology and Medicine 124:103959. doi:10.1016/j.compbiomed.2020.103959.
  • Yang, G., Cai, Y., and Reddy, C. K. 2018, July. Spatiotemporal check-in time prediction with recurrent neural network based survival analysis. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence.
  • Yeboah, J., C. Rodriguez, W. Qureshi, S. Liu, J. Carr, J. Lima, G. Hundley, and D. Herrington. 2016. Prognosis of low normal left ventricular ejection fraction in an asymptomatic population-based adult cohort: The multiethnic study of atherosclerosis. Journal of Cardiac Failure 22 (10):763–8. doi:10.1016/j.cardfail.2016.03.013.
  • Yu, B, and Y. Peng. 2008. Mixture cure models for multivariate survival data. Computational Statistics & Data Analysis 52 (3):1524–32. doi:10.1016/j.csda.2007.04.018.
  • Zahid, F. M., S. Ramzan, S. Faisal, and I. Hussain. 2019. Gender based survival prediction models for heart failure patients: A case study in Pakistan. PLoS One 14 (2):e0210602. doi:10.1371/journal.pone.0210602.
  • Zhu, M., J. Xia, X. Jin, M. Yan, G. Cai, J. Yan, and G. Ning. 2018. Class weights random forest algorithm for processing class imbalanced medical data. IEEE Access 6:4641–52. doi:10.1109/ACCESS.2018.2789428.
  • Zupan, B., J. Demšar, M. Kattan, and J. Beck. 1999. Machine learning for survival analysis: A case study on recurrence of prostate cancer. Artificial Intelligence Medicine 20 (1):59–75. doi: 10.1016/s0933-3657(00)00053-1.

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