Ensemble learning-based multimodal data analysis improving the diagnostic accuracy of Alzheimer's disease

Junjiang Wu; Hengchao Zhang; Xiaolong Zhu; Yan Zhang; Xuemei Ding; Hongqin Yang

doi:10.1117/12.2687618

27 November 2023 Ensemble learning-based multimodal data analysis improving the diagnostic accuracy of Alzheimer's disease

Junjiang Wu, Hengchao Zhang, Xiaolong Zhu, Yan Zhang, Xuemei Ding, Hongqin Yang

Author Affiliations +

Proceedings Volume 12770, Optics in Health Care and Biomedical Optics XIII; 1277030 (2023) https://doi.org/10.1117/12.2687618
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

Alzheimer's disease (AD) is a common neurodegenerative disease, whose early diagnosis is crucial for disease control and treatment. This study aims to explore the use of ensemble learning to analyze data from AD patients using multimodal inputs, including MRI image features extracted by convolutional neural networks (CNN), age, gender, APOE status and clinical functional scales. Firstly, we preprocess and extract the key image information features related to AD from MRI images. We then used multiple machine learning (ML) methods to build different classifiers, and combined these different classifiers by voting to obtain more accurate prediction results. Our method has been validated on a large AD patient database.The results demonstrated that the analysis of multimodal data can significantly improve the diagnostic accuracy of AD compared to single-mode data, while ensemble learning further improves the stability of the model.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junjiang Wu, Hengchao Zhang, Xiaolong Zhu, Yan Zhang, Xuemei Ding, and Hongqin Yang "Ensemble learning-based multimodal data analysis improving the diagnostic accuracy of Alzheimer's disease", Proc. SPIE 12770, Optics in Health Care and Biomedical Optics XIII, 1277030 (27 November 2023); https://doi.org/10.1117/12.2687618

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