Breast cancer prevention is now a global health concern and is no longer limited to Western countries [1–3]. A 2011 report released from the Department of Health, Taiwan, China indicated that breast cancer in women was the fourth most common cause of cancer-related deaths among Taiwan women and the mortality was increasing each year [4]. Therefore, exploring the factors affecting the 5-year mortality after breast cancer surgery is imperative.

Few studies have compared the artificial neural network (ANN), multiple logistic regression (MLR), and Cox regression prediction models in terms of internal validity (reproducibility), which is an essential performance metric [5, 6]. However, variable predictive models are insufficient to reliably predict the long-term survival of patients after surgery.

The ANN model used in the present study was a standard feed-forward, back-propagation neural network with three layers: an input layer, a hidden layer, and an output layer. A multilayer perceptron (MLP) network is an emerging tool for designing special classes of layered, feed-forward networks [7]. The input layer comprises source neurons, and the output layer comprises outcome neurons; these two layers connect the network to the outside world. Additionally, an MLP network typically has one or more additional layers of neurons referred to as hidden neurons because they are not directly accessible. The hidden neurons extract important features contained in the input data.

An MLP network is typically trained using a back-propagation algorithm with forward and backward phases [7]. The back-propagation learning algorithm is easily implemented, and its linear complexity in the synaptic weights of the network makes it computationally efficient. For optimal learning efficiency, the neurons are typically activated with both anti-symmetric functions (e.g., hyperbolic tangent function) and non-symmetric functions (e.g., logistic function). The following cross-validation technique is used to optimize the time when an MLP network training session “stops.” An estimation dataset is used for training the model, and a validation dataset is used for evaluating model performance. The neural network is subsequently optimized using the training dataset. Finally, a separate testing dataset is used to determine when training should stop to mitigate over-fitting. The training cycle is repeated until the testing error no longer decreases [8, 9].

Although substantially improved outcome prediction models have been developed for many surgical procedures in recent years, studies of breast cancer outcome prediction models have had major shortcomings [10, 11]. For example, few studies have used longitudinal data for more than 2 years. Moreover, no breast cancer outcome prediction studies have considered group differences in factors such as age, gender, and non-surgical treatment. The present study used the ANN, MLR, and Cox models to identify the most influential factors for prediction of breast cancer surgery outcomes. The accuracy of various predictive models was also compared and analyzed using a global sensitivity analysis to assess the relative weights of significant predictors. The predictive simulations performed in the present study are expected to improve healthcare policies in Taiwan, China and the development of decision-supporting systems. Therefore, the primary aim of the present study was to validate the use of the ANN model for predicting 5-year mortality of breast cancer patients after surgery. The secondary aim was to compare predictive capability between the ANN, MLR, and Cox models.

Performance indices were used to compare the three models in terms of accuracy in predicting breast cancer patient mortality. Overall, the mortality predicted using the ANN model was considerably more accurate than those predicted using the MLR and Cox models. When using actual outcome data for a performance comparison based on a simple outcome measure such as mortality, the ANN model clearly outperformed the MLR and Cox models constructed using the same limited number of clinical inputs.

Compared with reports that used data from a single medical center, the present registry study based on the data from BNHI, Taiwan, China provides a better overview of the current practice of breast cancer surgery. Unlike other single-center serial studies, the data from the present study and previous registry studies [15–17] provide an overview of the practices in large populations, avoiding referral bias or bias reflecting the practices of individual surgeons or institutions.

In recent years, studies have consistently shown that the ANN prediction model was superior to the MLR and Cox models [18–20]. Statistical analyses have also confirmed the advantages and preferred characteristics of the ANN model. The high fault tolerance of the ANN model also facilitates the appropriate and accurate processing of incomplete or noise-added inputs [13]. Moreover, highly correlated and non-normally distributed data can be used to construct linear and non-linear ANN models with broad applications in large-scale medical databases. In the medical field, the ANN model is commonly used for clinical research, especially for prognosis prediction [18–20]. Comparisons of various models in the present study suggested that the ANN predictive model was superior for expanding predictor variables to facilitate the systematic analysis of various diseases, for evaluating the effectiveness of research methods, and for comprehensively predicting mortality. The established predictive model can also be extended to other cancer types.

We conducted a global sensitivity analysis to assess the weights of significant predictors affecting the 5-year postoperative mortality of breast cancer patients. We found that surgery volume of surgeon was the most important predictor of 5-year mortality of breast cancer patients, consistent with the results of other reports [21, 22] showing that surgeons with high surgery volumes consistently achieve superior outcomes of breast cancer surgery than surgeons with low surgery volumes. Therefore, the treatment strategies made by these surgeons should be carefully analyzed and emulated. If the 5-year mortality is considered a benchmark, then breast cancer surgery volume of surgeon, a major predictor of postoperative outcomes, is crucial. Clearly, the outcomes of surgical procedures depend not only on clinical management but also on the skill and experience of individual surgeons. Moreover, surgeons with high surgery volumes in hospitals with high surgery volumes are most likely to achieve good patient outcomes because they are well supported by highly skilled interdisciplinary care teams [21].

Breast cancer surgery volumes of surgeon and hospital are the selection criteria typically considered by individuals seeking healthcare services. Patients also tend to seek treatment at large medical centers and by renowned, authoritative physicians [21, 22]. The results of the present study indicated that the surgery volumes of surgeon and hospital were negatively correlated with the 5-year mortality of breast cancer patients. That is, compared with their counterparts with low surgery volumes, hospitals and surgeons with high surgery volumes achieved higher patient survival rates. This finding provides a reference for healthcare institutions when assisting surgeons in cultivating relevant surgical experience.

The positive correlation observed between breast cancer mortality and age at onset requires further study. Strategies for improving screening rates to achieve early detection and treatment should also be investigated and compared. These data would provide a valuable reference for screening and for integrating therapeutic methods in predictive models for medical decision making.

Patients who undergo surgery for breast cancer are often burdened by cancer-related comorbidities that increase the risk of poor surgical outcomes, such as long hospitalization, high mortality, and high treatment costs [18–22]. In the present study, the statistical data for postoperative outcomes also revealed that 5-year mortality increased with CCI.

This long-term follow-up study analyzed patient data obtained from a database in Taiwan, China. In addition to enhancing the variance analysis of the correlation between treatment and survival, predictive models also have many applications in clinical care. The methods developed in the present study can also be used at healthcare institutions to evaluate the effectiveness of screening methods for early detection and treatment. Because of its accuracy in predicting breast cancer mortality, the proposed ANN model can be used to provide further data supporting the consensus view regarding the importance of breast cancer screening and the need for prompt and appropriate action. Beyond breast cancer, broader applications of this method can also facilitate the formulation and promotion of healthcare policies and the development of decision-supporting systems in Taiwan, China, which would ultimately enhance the health of all citizens. Additional studies are needed to determine the true clinical relevance of the ANN model and to clarify whether clinicians can effectively use the model to predict prognosis and optimize the surgical management for patients.

The present study has several limitations inherent in any large database analysis. First, the clinical picture obtained in the analysis of claims data is not as precise as that obtained in the analysis of a prospective clinical trial data, reflecting potential errors in the coding of primary diagnoses and surgical modalities. Second, complications associated with breast cancer surgical procedures were not assessed, limiting the validity of the comparison. Third, the specific focus on 5-year mortality as the endpoint of this prediction tool may limit the overall clinical use of the ANN model to a small subset of patients who have a high likelihood of death within 5 years. Fourth, only three models were used to predict 5-year survival after breast cancer surgery. Finally, other outcomes, such as patient-reported quality of life, were not compared because the relevant data were not included in the database. However, considering the robust magnitude and the statistical significance of the effects in the present study, these limitations are unlikely to compromise the validity of the results.

Compared with the conventional MLR and Cox models, the ANN model in the present study was more accurate in predicting 5-year mortality of patients after breast cancer surgery and showed higher overall performance indices. The predictors analyzed in the present study could be addressed in preoperative and postoperative health care consultations to educate candidates for breast cancer surgery in the expected course of recovery and health outcomes. The international academic community has commended the researchers in Taiwan, China for the novel findings obtained by database studies. However, these studies can be further improved by including additional clinical variables, which could yield additional research findings and increase precision. Such findings can therefore provide a vital and indicative basis for promoting healthcare policies in Taiwan, China.