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Early diagnosis model of Alzheimer’s Disease based on sparse logistic regression

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Abstract

Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI) are critical for the effective treatment of AD. However, compared with AD classification tasks, predicting the conversion of MCI to AD is relatively difficult. as there are only minor differences among MCI groups. What’s more, in brain imaging analysis, the high dimensionality and relatively small number of subjects brings challenges to computer-aided diagnosis of AD and MCI. Many previous researches focused on the identification of imaging biomarkers for AD diagnosis. In this paper, we introduce sparse logistic regression for the early diagnosis of AD. Sparse logistic regression (SLR) uses L1/2 regularization to impose a sparsity constraint on logistic regression. The L1/2 regularization is considered a representative of Lq regularization, where fewer but informative key brain regions are applied for the classification of AD/MCI. We evaluated the SLR on 197 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results showed that the SLR improves the classification performance of AD/MCI compared other classical methods.

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Acknowledgments

This work is partially supported by National Nature Science Foundation of China (71971190); Ministry of Education Humanities and Social Science Project (11YJCZH021, 15YJCZH111). Shandong Social Science Planning Research Project (17CHLJ41, 16CTQJ02, 18CHLJ34).

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Authors and Affiliations

  1. School of Information Science and Engineering, Qufu Normal University, Rizhao, 276800, China

    Ruyi Xiao, Xinchun Cui & Yiquan Zhang

  2. Business School, Shandong Normal University, Jinan, 250014, China

    Hong Qiao

  3. School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China

    Xiangwei Zheng

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  1. Ruyi Xiao
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Correspondence to Xinchun Cui.

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Xiao, R., Cui, X., Qiao, H. et al. Early diagnosis model of Alzheimer’s Disease based on sparse logistic regression. Multimed Tools Appl 80, 3969–3980 (2021). https://doi.org/10.1007/s11042-020-09738-0

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