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. 2024 Dec 22;16(12):e76227.
doi: 10.7759/cureus.76227. eCollection 2024 Dec.

Optimal Machine Learning Models for Developing Prognostic Predictions in Patients With Advanced Cancer

Jun Hamano  1 , Ayano Takeuchi  2 , Tomoya Keyaki  3 , Hidemasa Nose  4 , Kenichi Hayashi  5
Affiliations

Optimal Machine Learning Models for Developing Prognostic Predictions in Patients With Advanced Cancer

Jun Hamano et al. Cureus. .

Abstract

Context: Accurate prognosis prediction for cancer patients in palliative care is critical for clinical decision-making and personalized care. Traditional statistical models have been complemented by machine learning approaches; however, their comparative effectiveness remains underexplored.

Objectives: To assess the prognostic accuracy of statistical and machine learning models in predicting 30-day survival in patients with advanced cancer using objective data, such as the result of the blood test.

Methods: A secondary analysis of the Japan-Prognostic Assessment Tools Validation (J-ProVal) study was performed from September 2012 to April 2014. We used data from 58 palliative care services in Japan and enrolled 915 patients. Four models, fractional polynomial (FP) regression, Kernel Fisher discriminant analysis (KFDA), Kernel support vector machine (KSVM), and XGBoost, were compared using 17 objective clinical characteristics. Models were evaluated with the area under the receiver operating characteristic curve (AUC) as the primary metric.

Results: The KSVM model demonstrated the highest predictive accuracy (AUC: 0.834), outperforming the FP model (AUC: 0.799). XGBoost showed comparatively lower performance; however, it was likely limited by the size of the dataset.

Conclusions: Machine learning, particularly KSVM, has high predictive accuracy in palliative care when sufficient data are available. However, our findings suggest that traditional statistical models offer advantages in stability and interpretability, underscoring the importance of tailored model selection based on data characteristics.

Keywords: advanced cancer patients; machine learning models; palliative and end-of-life care; prognostic prediction; traditional statistical models.

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Conflict of interest statement

Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Institutional Review Board of Seirei Mikatahara General Hospital issued approval 201210. This study was conducted in accordance with the ethical standards of the Declaration of Helsinki and the ethical guidelines for research presented by the Ministry of Health, Labour, and Welfare of Japan. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: This work was supported in part by JSPS KAKENHI Grant Number 22H03305. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. Example of nonlinear transformation (colors indicate groups)
Data that cannot be discriminated linearly
Figure 2
Figure 2. Example of nonlinear transformation (colors indicate groups)
Data after transformation to allow for linear discrimination
See this image and copyright information in PMC

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