Artificial neural network models based on questionnaire survey for prediction of breast cancer risk among Chinese women in Shanghai

Objective: To develop a breast cancer risk predictive model based on questionnaire survey data using artificial neural network (ANN) approach, and thus to provide an effective tool for initial screening of breast cancer. Methods: During the period of 2008-2012, an organized breast cancer screening project was conducted among 15 148 healthy women at age of 35-74 years in Minhang District of Shanghai, China. The information on demographic characteristics, reproductive factors, history of any breast diseases, and family history of breast cancer was collected by in-person interview using a structured questionnaire. Sixty-six breast cancer cases were identified through pathological examination. Logistic backward regression was used to select significant risk factors. An ANN model was developed and tested by Feed-forward Networks and limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method. Results: The variables including age, age at menarche, family history of breast cancer, breast lumps, nodules or thickening, years since first delivery, and days of fatty meat intake per week were included in ANN model. In the training set, the model achieved an accuracy of 66.5% [95% confidence interval (CI): 65.6-67.4], a sensitivity of 63.8% (95% CI: 50.1-77.6), a specificity of 66.5% (95% CI: 65.6-67.4), and the area under receiver operating characteristic curve (AUC) of 0.706 (95% CI: 0.635-0.777). In the test setting, the model had an accuracy of 64.9% (95% CI: 63.5-66.3), a sensitivity of 79.0% (95% CI: 60.6-97.3), a specificity of 64.8% (95% CI: 63.4-66.2) and an AUC of 0.762 (95% CI: 0.655-0.869). Conclusion: The ANN model based on questionnaire survey data has predictive value of breast cancer risk in Chinese women in Shanghai, and has potential to be used in risk self-assessment and preliminary screening in population. DOI:10.3781/j.issn.1000-7431.2018.33.163