Abstract
BackgroundBreast cancer imposes a considerable burden on both the health care system and society, and becomes increasingly severe among women in China. To reduce the economic burden of this disease is crucial for patients undergoing the breast cancer surgery, hospital managers, and medical insurance providers. However, few studies have evidenced the prediction of the total hospital expenses (THE) for breast cancer surgery. The aim of the study is to predict THE for breast cancer surgery and identify the main influencing factors.MethodsData were retrieved from the first page of medical records of 3699 patients undergoing breast cancer surgery in one tertiary hospital from 2017 to 2018. Multiple liner regression (MLR), artificial neural networks (ANNs), and classification and regression tree (CART) were constructed and compared.ResultsThe dataset from 3699 patients were randomly divided into training and test sets at a 70:30 ratio (2599 and 1100 records, respectively). The average total hospital expenses were 12520.54 ± 7844.88 ¥ (US$ 1929.20 ± 1208.11). MLR results revealed six factors to be significantly associated with THE: age, LOS, type of disease, having medical insurance, minimally invasive surgery, and receiving general anesthesia. After comparing three models, ANNs was the best model to predict THEs in patients undergoing breast cancer surgery, and its strong predictive performance was also validated.ConclusionsTo reduce the THEs, more attention should be paid to related factors of LOS, major and minimally invasive surgeries, and general anesthesia for these patient groups undergoing breast cancer surgery. This may reduce the information asymmetry between doctors and patients and provide more reliable cost, practical inpatient medical consumption standards and reimbursement standards reference for patients, hospital managers, and medical insurance providers ,respectively.
Highlights
Breast cancer comprises different histopathological entities, which vary from benign tumors to highly aggressive cancers
Artificial neural network (ANN) and classification and regression tree (CART) models have been used for hospitalization cost prediction for various diseases, including genitourinary diseases [12], colorectal cancer [13], and gastric cancer [14]
Few studies have evidenced the prediction of the total hospital expenses for breast cancer surgery
Summary
Breast cancer comprises different histopathological entities, which vary from benign tumors to highly aggressive cancers Prediction of the total hospital expenses for patients undergoing breast cancer surgery is essential as it may guide medical resource allocation and health policy decision-making in low- and middle-income countries, such as China. Data mining techniques, such as decision trees, neural networks, and support vector machines, have been increasingly adopted in the medical field These techniques derive internal relations and rules from the sample data and help predict and analyze unknown or unobservable data. We used a model mixed with ANNs and CART to predict total hospital expenses for breast cancer surgery and analyze the main influencing factors. The aim of the study is to predict THE for breast cancer surgery and identify the main influencing factors
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