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Open Access

Multi-Task Learning for Alzheimer’s Disease Diagnosis and Mini-Mental State Examination Score Prediction

Jin Liu1,2( ), Xu Tian1, Hanhe Lin3, Hong-Dong Li1, Yi Pan4( )
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
Xinjiang Engineering Research Center of Big Data and Intelligent Software, School of Software, Xinjiang University, Urumqi 830091, China
School of Science and Engineering, University of Dundee, Dundee, DD1 4HN, UK
Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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Abstract

Accurately diagnosing Alzheimer’s disease is essential for improving elderly health. Meanwhile, accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer’s disease. However, most of the existing methods perform Alzheimer’s disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two tasks. To address this challenging problem, we propose a novel multi-task learning method, which uses feature interaction to explore the relationship between Alzheimer’s disease diagnosis and mini-mental state examination score prediction. In our proposed method, features from each task branch are firstly decoupled into candidate and non-candidate parts for interaction. Then, we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches, which can promote the learning of each task. We validate the effectiveness of our proposed method on multiple datasets. In Alzheimer’s disease neuroimaging initiative 1 dataset, the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86% and 2.5, respectively. Experimental results show that our proposed method outperforms most state-of-the-art methods. Our proposed method enables accurate Alzheimer’s disease diagnosis and mini-mental state examination score prediction. Therefore, it can be used as a reference for the clinical diagnosis of Alzheimer’s disease, and can also help doctors and patients track disease progression in a timely manner.

Keywords

multi-task learningAlzheimer’s disease diagnosismini-mental state examination score prediction

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Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 828-842
DOI: 10.26599/BDMA.2024.9020025
Cite this article:
Liu J, Tian X, Lin H, et al. Multi-Task Learning for Alzheimer’s Disease Diagnosis and Mini-Mental State Examination Score Prediction. Big Data Mining and Analytics, 2024, 7(3): 828-842. https://doi.org/10.26599/BDMA.2024.9020025

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Received: 13 January 2024
Revised: 30 March 2024
Accepted: 01 April 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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