Classification Using Deep Transfer Learning on Structured Healthcare Data

Ayda Farhadi, David Chen, Rozalina G. McCoy, Christopher Scott, Ping Ma, Celine M. Vachon, Jingyi Zhang, Che Ngufor, John A. Miller

Abstract: In healthcare, building a supervised learning system faces the challenge of access to a large, labeled dataset. To overcome this problem, we propose a deep transfer learning method that addresses imbalanced data problems in healthcare, focusing on structured data. We use publicly available breast cancer datasets to generate a source model and transfer learned concepts to predict high-grade malignant tumors in patients diagnosed with breast cancer at Mayo Clinic. The diabetes dataset is then used to generalize the transfer learning idea. We compare our results with state-of-the-art techniques and demonstrate the superiority of our proposed methods. Our experiments on breast cancer data under simulated class imbalanced settings further demonstrate the proposed method's ability to handle different degrees of class imbalance. We conclude that deep transfer learning on structured data can efficiently address imbalanced class and poor performance learning on small dataset problems in clinical research.