Abstract
Nowadays, emerging mobile medical technology and disease prevention become new trends of disease prevention and control. Based on this technology, we present disease prediction models based on transfer learning. Breast cancer disease data has been used to build our model. According to the neural networks, the basic model has been provided. With unlabeled data, transfer learning is a appropriate way to revise the module to increase accuracy. The test results show that the algorithm is suitable for data classification, especially for unlabeled health data.
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Acknowledgments
This paper was partially supported by National Sci-Tech Support Plan 2015BAH10F01, NSFC grant U1509216,61472099, the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Heilongjiang Province LC2016026 and MOE-Microsoft Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology.
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Song, Y., Yue, T., Wang, H., Li, J., Gao, H. (2017). Disease Prediction Based on Transfer Learning in Individual Healthcare. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_10
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DOI: https://doi.org/10.1007/978-981-10-6385-5_10
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