@inproceedings{eec7bd89f15d43b8b418598eb012b2a5,
title = "Classification Using Deep Transfer Learning on Structured Healthcare Data",
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.",
keywords = "Breast cancer, Class imbalance, Deep learning, Deep transfer learning, SMOTE",
author = "Ayda Farhadi and David Chen and Rozalina McCoy and Christopher Scott and Ping Ma and Vachon, {Celine M.} and Jingyi Zhang and Che Ngufor and Miller, {John A.}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 ; Conference date: 05-12-2023 Through 08-12-2023",
year = "2023",
doi = "10.1109/SSCI52147.2023.10371847",
language = "English (US)",
series = "2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1560--1565",
booktitle = "2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023",
}