Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques|RISS 상세보기

다국어 초록 (Multilingual Abstract)

Objectives: The objective of this research is to apply machine learning (ML) algorithms to predict the survival of cervical cancer patients. The aim was to address the limitations of traditional statistical methods, which often fail to provide accurate answers due to the complexity of the problem. Methods: This research employed visualization techniques for initial data understanding. Subsequently, ML algorithms were used to develop both classification and regression models for survival prediction. In the classification models, we trained the algorithms to predict the time interval between the initial diagnosis and the patient’s death. The intervals were categorized as “<6 months,” “6 months to 3 years,” “3 years to 5 years,” and “>5 years.” The regression model aimed to predict survival time (in months). We used attribute weights to gain insights into the model, highlighting features with a significant impact on predictions and offering valuable insights into the model’s behavior and decision-making process. Results: The gradient boosting trees algorithm achieved an 81.55% accuracy in the classification model, while the random forest algorithm excelled in the regression model, with a root mean square error of 22.432. Notably, radiation doses around the affected areas significantly influenced survival duration. Conclusions: Machine learning demonstrated the ability to provide high-accuracy predictions of survival periods in both classification and regression problems. This suggests its potential use as a decision-support tool in the process of treatment planning and resource allocation for each patient.

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참고문헌 (Reference)

1 Matsuo K, "Survival outcome prediction in cervical cancer : Cox models vs deep-learning model" 220 (220): 381-, 2019

2 Tharavichitkul E, "Survival outcome of cervical cancer patients treated by imageguided brachytherapy : a ‘real world’ single center experience in Thailand from 2008 to 2018" 63 (63): 657-665, 2022

3 Lee SH, "Survival of cervical cancer patients in Brunei Darussalam : 2002-2017" 9 (9): e16080-, 2023

4 Kleinbaum DG, "Survival analysis a self-learning text" Springer 2012

5 Rebernick RJ, "Survival analyses : a statistical review for surgeons" 34 (34): 1388-1394, 2022

6 Hassanpour SH, "Review of cancer from perspective of molecular" 4 (4): 127-129, 2017

7 Tapak L, "Prediction of survival and metastasis in breast cancer patients using machine learning classifiers" 7 (7): 293-299, 2019

8 Lynch CM, "Prediction of lung cancer patient survival via supervised machine learning classification techniques" 108 : 1-8, 2017

9 Adeoye J, "Prediction models applying machine learning to oral cavity cancer outcomes : a systematic review" 154 : 104557-, 2021

10 Wang Y, "Predicting postoperative liver cancer death outcomes with machine learning" 37 (37): 629-634, 2021