Bayesian Model Prediction for Breast Cancer Survival: A Retrospective Analysis

Abstract

Objective: Over the recent years, machine learning (ML) models have been increasingly used in predicting breast cancer survival because of improvements in ML algorithms. However, cancer researchers still face a significant challenge in accurately predicting breast cancer patients' survival rates. The purpose was to predict breast cancer survival using a Bayesian network.

Materials and methods: This retrospective study included 2,995 patients diagnosed with breast cancer and subsequently hospitalized between January 1, 2012, and December 30, 2024. SPSS Modeler version 18.0 was used to build prediction models. The data were randomly split into a training set (2,097 cases, 70%) and a test set (898 cases, 30%) for developing the Bayesian network model and predicting the overall survival of patients diagnosed with breast cancer. The model included demographic variables (age, marital status, and governorate), laboratory/clinical variables (hemoglobin level, white blood cell count, presence of hypertension, and diabetes mellitus) and the outcome variable, patient survival status (binary value: survived/died). The discriminative ability of models was evaluated by accuracy and the area under the curve (AUC) in terms of superior predictive performance for breast cancer outcomes.

Results: The Bayesian model exhibited the best discriminatory performance among the nine models, with an AUC of 0.859 and the highest accuracy of 96.661%. In the context of feature importance, white blood cell value at the time of diagnosis was the most important feature for predicting the survival of breast cancer. Patients who had below-normal hemoglobin and above-normal white blood count values had a higher death probability than patients who had normal white blood count and hemoglobin values. The presence of hypertension and diabetes mellitus in patients with breast cancer led to a reduced survival probability.

Conclusion: The Bayesian model outperformed the other models in predicting the survival probability of breast cancer. Routine laboratory testing and demographic data can be included in a ML model to predict breast cancer survival. Accurate prediction of breast cancer survival is vital for clinical decision-making.

Keywords: Bayesian model; breast cancer; machine learning; prediction models; survival.

Figure 1

Patient flow chart

Figure 2

Kaplan-Meier survival plots and Hazard ratios

Figure 3

The structure of Bayesian network model

Figure 4

Predictors’ importance for the outcome of breast cancer