DISCUSSION
ovarian cancer, hospital‐based, breast cancer, cancer, abortions, abortion, non‐genetic
OVARIAN CANCER, CANCER, BREAST CANCER
In the current study, we created a moderate performing risk prediction model for breast cancer in southeast China. We confirmed that age, age at menarche, age at first live birth, number of births, abortions, family history of breast cancer and ovarian cancer and prior breast surgery are strong risk predictors for breast cancer. In addition, the performance of the model established in this study was better than models based on CKB score, Gail score and Tyrer–Cuzick score. However, the logistic model have no statistically significant difference between the LASSO model and SVM model. Our study also suggested that adding PRS can increase the predictive ability of the model.Our study confirmed the association between several reproductive factors and breast cancer, including age at menarche, age at first live birth and number of births.Abortion is a substantial and persistent risk factor for breast cancer in Chinese women, as further evidenced by the current study. An earlier meta‐analysis in China found that when the rate of abortion rose, so did the risk of breast cancer.With the best AUC values of 0.735 and 0.762, the Gail score and the Tyrer–Cuzick score are the two most popularly used breast cancer risk scores in American and European countries.In China, several breast cancer risk assessment models have been developed based on data from other regions, with AUC varying from 0.573 to 0.658.Few models of breast cancer risk incorporate both non‐genetic and genetic components.SVM previously demonstrated the best performance when compared to other machine learning techniques for predicting breast cancer risk,Several studies have shown that developing risk models based on ER status of breast cancer may improve prediction and enable targeted screening and prevention in the future.This study has several advantages. Both genetic and non‐genetic factors were included in the model, which to some extend improved the power of the model. In addition, this study also applied machine learning methods, including LASSO and SVM, to compare different models. Other than the advantages mentioned above, this study has several limitations. First of all, this study is a hospital‐based case–control study with potential selection bias from the participants. We have a small proportion of patients with old age at diagnosis and late stage cancer. Therefore, our prediction model could not be generalized to these women. The reporting on the reproductive and familial risk factors could have been different between patients and controls due to recall bias. Further independent validation studies based on prospective cohorts are needed to test its accuracy and robustness. Thirdly, image‐based risk predictors for breast cancer such as mammographic density and micro‐calcification were not collected in current study, which might have reduced the performance of the model. BRAC mutations were not genotyped in our dataset, and therefore considered as missing in the calculation of Tyrer–Cuzick score, which might have reduced the performance of the model. However, as these variants are too rare in Chinese women, even in the cases,