Abstract
Prostate cancer is the most frequent malignancy and the leading cause of cancer-related mortality globally. A precise survival estimate is required for the effectiveness of treatment to minimize mortality rate. A remedial strategy can be planned under the anticipated survival state. Machine Learning (ML) methods have recently garnered considerable interest, particularly in developing data-driven prediction models. Unfortunately prostate cancer has received less attention to such studies. In this paper, we have built models using machine learning methods to predict whether a patient with prostate cancer would live for five years or not. Compared to prior studies, correlation analysis, a substantial quantity of data, and a unique track with hyperparameter adjustment boost the performance of our model. The SEER(Surveillance, Epidemiology, and End Results) database provided the data for developing these models. The SEER program gathers and disseminates cancer data to mitigate the disease’s effect. We analyzed prostate cancer patients’ five-year survival state using about seven prediction models. Gradient Boosting, Light Gradient Boosting Machine, and Ada Boost algorithms are identified as top-performed prediction models. Among them, a tuned prediction model using the Gradient Boosting algorithm outperforms others, with an accuracy of 88.45% and found fastest among the other models.
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References
Wen, H., Li., S, et al.: Comparision of four machine learning techniques for the prediction of prostate cancer survivability. In: 15th International Computer Conference on Wavelet Active Media Technology and Information Processing, vol. I5, pp. 112–116 (2018)
Delen, D., Patil, N.: Knowledge extraction from prostate cancer data. In: 39th Annual Hawaii International Conference on System Sciences (HICSS 2006), vol. I5, pp. 92b–92b (2006)
Lynch, C.M., et al.: Prediction of lung cancer patient survival via supervised machine learning classification techniques. Int. J. Med. Inf. 108, 1–8 (2017)
Cirkovic, B.R.A., et al.: Prediction models for estimation of survival rate and relapse for breast cancer patients. In: 15th International Conference on Bioinformatics and Bioengineering (BIBE), vol. I5, pp. 1–6 (2015)
Montazeri, M., et al.: Machine learning models in breast cancer survival prediction. Technol. Health Care 24, 31–42 (2016)
Bellaachia A, Guven E.: Predicting breast cancer survivability using data mining techniques, pp 10–110 (2006)
Endo, A., et al.: Comparison of seven algorithms to predict breast Cancer survival. Int. J. Biomed. Soft Comput. Human Sci. 13, 11–16 (2008)
Delen, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artifi. Intell. Med. 34, 113–127 (2005)
Mourad, M., et al.: Machine learning and feature selection applied to SEER data to reliably assess thyroid cancer prognosis. Scient. Reports 10, 1–11 (2020)
Pradeep, K., Naveen, N.: Lung cancer survivability prediction based on performance using classification techniques of support vector machines, C4. 5 and Naive Bayes algorithms for healthcare analytics. Proc. Comput. Sci. 132, 412–420 (2018)
Agrawal, A., et al.: Lung cancer survival prediction using ensemble data mining on SEER data. Sci. Program. 20, 29–42 (2012)
Lundin, M., et al.: Artificial neural networks applied to survival prediction in breast cancer. Oncology 57, 281–286 (1999)
Thongkam, J., et al.: Breast cancer survivability via AdaBoost algorithms. In: Proceedings of the second Australasian Workshop on Health Data and Knowledge Management, vol. 80, pp 55–64, Citeseer (2008)
Polash, Md.S.I, Hossen, S., et al.: Functionality testing of machine learning algorithms to anticipate life expectancy of stomach cancer patients. In: 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICA5), pp 1–6 (2022)
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Polash, M.S.I., Hossen, S., Haque, A. (2023). Five-Year Life Expectancy Prediction of Prostate Cancer Patients Using Machine Learning Algorithms. In: Patel, K.K., Santosh, K.C., Patel, A., Ghosh, A. (eds) Soft Computing and Its Engineering Applications. icSoftComp 2022. Communications in Computer and Information Science, vol 1788. Springer, Cham. https://doi.org/10.1007/978-3-031-27609-5_25
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DOI: https://doi.org/10.1007/978-3-031-27609-5_25
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