The Application of Data Imputation and Deep Learning Network in the Papillary Thyroid Carcinoma Classification
Wenxin Jiang, Xiaotong Chen, Ning Lv, Miao Rao, Yanvan Yu, Weibao Qiu, Jianming Li · 2021
Missing values, diversified data types and insufficient sample size have become obstacles to clinical data analysis. In this work, we proposed a machine learning pipeline to analyze the metastasis rate of papillary thyroid carcinoma using B mode ultrasound images and incomplete clinical features simultaneously. Missing values in the clinical features were imputed by the multiple imputation algorithm and US images were analyzed by a deep transfer learning network. We applied the support vector machine to concatenate two types of features and made final predictions. Our proposed method achieves an AUC of 0.76, a sensitivity of 0.67, a specificity of 0.75 and an accuracy of 0.72 under 10-fold cross validation. These results are better than the transfer learning network based on US images (AUC=0.74) and the SVM method based on the clinical features (AUC=0.73).