A Multi-task Learning Framework for Automatic Early Detection of Alzheimer’s

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.