A Research on Machine Learning for Predicting Survival Time in Cancer Patients

Binghua Xi

doi:10.62051/6bzv6x65

Authors

Binghua Xi

DOI:

https://doi.org/10.62051/6bzv6x65

Keywords:

Survival prediction; machine learning; predictive Models.

Abstract

Cancer remains one of the leading causes of mortality worldwide. Predicting the survival of cancer patients holds immense significance in guiding treatment decisions and enhancing patient quality of life. To provide a comprehensive understanding of the application of machine learning methods and to explore future research directions in survival prediction, this paper reviews the current landscape of machine learning algorithms in this domain. The paper discusses the significance and challenges associated with predicting the survival time of cancer patients. It begins with an introduction highlighting the importance of this prediction task in the medical domain. Traditional methods utilized for survival prediction are then explored, shedding light on their strengths and limitations. Following this, the paper delves into the application of traditional machine learning approaches for survival prediction. Subsequently, the focus shifts towards deep learning algorithms and their role in survival prediction. In conclusion, the paper identifies potential avenues for further research in this field, paving the way for continued advancements in cancer survival prediction.

Downloads

Download data is not yet available.

References

H. Zhang. Construction of prognosis survival model of pancreatic cancer after radical surgery based on machine learning. Southern Medical University, 2024.

M. Karabacak, K. Margetis A Machine Learning-Based Online Prediction Tool for Predicting Short-Term Postoperative Outcomes Following Spinal Tumor Resections. Cancers (Basel). 2023 Jan 28;15(3):812.

PN. Butow, JM. Clayton, R. Epstein. Prognostic Awareness in Adult Oncology and Palliative Care. J Clin Oncol. 2020 Mar 20;38(9):877-884.

Q. Shao. Clinical study on the application value of LMR and AFR in predicting long-term survival after pancreatic ductal adenocarcinoma . Lanzhou University, 2023.

Y. Huang et al. Application of machine learning in predicting survival outcomes involving real-world data: a scoping review. BMC Med Res Methodol. 2023 Nov 13;23(1):268.

X. Hou. Study on Survival Analysis Model based on XGBoost. Daliann Maritime University,2023.

L. Xu ,L Cai ,Z Zhu , G.Chen . Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma. BMC Endocr Disord. 2023 Jun 5;23(1):129.

D Zhou et al. A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy. BMC Cancer. 2021 Mar 8;21(1):246.

W. Chen. Cancer diagnosis and prognosis based on Deep learning. Xi'an University of Architecture and Technology,2023.

E. Badisy I et al. Risk factors affecting patients survival with colorectal cancer in Morocco: survival analysis using an interpretable machine learning approach. Sci Rep. 2024 Feb 12;14(1):3556.

Y. Liu et al. Use of machine learning to predict bladder cancer survival outcomes: a systematic literature review. Expert Rev Pharmacoecon Outcomes Res. 2023 Jul-Dec;23(7):761-771.

R. Shibaki et al. Machine learning analysis of pathological images to predict 1-year progression-free survival of immunotherapy in patients with small-cell lung cancer. J Immunother Cancer. 2024 Feb 15;12(2):e007987.

M. Mahootiha et al. Multimodal deep learning for personalized renal cell carcinoma prognosis: Integrating CT imaging and clinical data, Computer Methods and Programs in Biomedicine, Volume 244,2024,107978.

Y. Ma et al. Comparison of differentiation performance between survival data machine Learning and COX model based on benchmark experiment. Modern preventive medicine,2023,50(13):2344-2348+2368.