Abstract
Alzheimer’s disease is a degenerative brain disease which threatens individuals’ living and even lives. In this paper, we develop a simple and inexpensive solution to perform early detection of Alzheimer’s, based on the individual’s background and behavioral data. To alleviate the data sparsity and feature misguidance problems, we propose a novel multi-task learning framework and a pairwise analysis strategy. Extensive experiments show that the proposed framework outperforms the state-of-the-art methods with higher prediction accuracy.
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References
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Acknowledgments
This research is supported in part by NSFC (No. 61772341, 61472254) and STSCM (No. 18511103002). This work is also supported by the Program for Changjiang Young Scholars in University of China, the Program for China Top Young Talents, the Program for Shanghai Top Young Talents, and Shanghai Engineering Research Center of Digital Education Equipment.
The NACC database is funded by NIA/NIH Grant U01 AG016976.
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Xu, N., Shen, Y., Zhu, Y. (2019). A Multi-task Learning Framework for Automatic Early Detection of Alzheimer’s. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_20
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DOI: https://doi.org/10.1007/978-3-030-18590-9_20
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