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.
CITATION STYLE
Xu, N., Shen, Y., & Zhu, Y. (2019). A Multi-task Learning Framework for Automatic Early Detection of Alzheimer’s. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11448 LNCS, pp. 240–243). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_20
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