Healthc Inform Res.  2024 Jan;30(1):60-72. 10.4258/hir.2024.30.1.60.

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

• Chanudom I
• Tharavichitkul E
• Laosiritaworn W

• KMID: 2551861
• DOI: http://doi.org/10.4258/hir.2024.30.1.60

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.

Keyword

Machine Learning, Data Visualization, Uterine Cervical Neoplasms, Survival Rate, Disease Attributes

Figure

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  Figure 1 Visualization of variables within the dataset: (A) age distribution of patients, (B) distribution of tumor size, (C) number of patients and number of deaths for each tumor stage, and (D) the relationship between patient mortality and side effects in different areas at different levels.

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  Figure 2 Amount of data for each target variable for classification model modeling.

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  Figure 3 Research framework.

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  Figure 4 Artificial neural network architecture of the classification model.

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  Figure 5 Comparison of the actual survival time and the predicted time when using each model: (A) decision tree (DT), (B) random forest (RF), (c) gradient boosting tree (GBT), (D) artificial neural network (ANN), and (E) k-nearest neighbor (KNN).

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  Figure 6 Attribute weight values in both classification and regression. See Table 1 for the full-term of the attribute.

Reference

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