Classification Using Transfer Learning on Structured Healthcare Data

Recently deep learning has been used as a new classification platform and has been applied to many domains. In some domains such as bioinformatics and healthcare constructing a large-scale well annotated data-set is very difficult. As such labeled data are limited. This structured data in healthcare are small data-set and because of that deep learning approaches do not perform well on their classification. Transfer learning relaxes the hypothesis that learning should occur purely based on specific data-sets, which motivates us to use transfer learning to solve the problem of insufficient training data. In this dissertation, I introduce my efforts toward creating a complete, fully automated, and efficient deep transfer learning method to handle the imbalanced data of breast cancer. I compared our results with state-of-the-art techniques for addressing problems of imbalanced learning, poor performance learning, and confirmed the superiority of the proposed methods. I conducted a meta-analysis to analyze the status of healthcare-related Transfer Learning(TL) studies in terms of the study targets, TL model(s) used, Healthcare data, type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how TL has been applied for improving the accuracy of diagnosis in healthcare including images, text, audio, video and structured Electronic Health Record data classification. I further present my deep transfer learning model to improve the accuracy of classification in diabetes disease. Finally, I demonstrate the significant performance gains of our model compared to state of art techniques for classification. Based on the experimental results, we

concluded that the proposed deep transfer learning on

structured data can be used as an efficient method to handle

imbalanced classes and poor performance learning on small dataset problems in clinical research.