Multiclass diagnosis of stages of Alzheimer's disease using linear discriminant analysis scoring for multimodal data

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and mild cognitive impairment (MCI) is a transitional stage between normal control (NC) and AD. A multiclass classification of AD is a difficult task because there are multiple similarities between neighboring groups. The perf...

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Veröffentlicht in:Computers in biology and medicine 2021-07, Vol.134, p.104478-104478, Article 104478
Hauptverfasser: Lin, Weiming, Gao, Qinquan, Du, Min, Chen, Weisheng, Tong, Tong
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Sprache:eng
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Zusammenfassung:Alzheimer's disease (AD) is a progressive neurodegenerative disease, and mild cognitive impairment (MCI) is a transitional stage between normal control (NC) and AD. A multiclass classification of AD is a difficult task because there are multiple similarities between neighboring groups. The performance of classification can be improved by using multimodal data, but the improvement could be limited with inefficient fusion of multimodal data. This study aims to develop a framework for AD multiclass diagnosis with a linear discriminant analysis (LDA) scoring method to fuse multimodal data more efficiently. Magnetic resonance imaging, positron emission tomography, cerebrospinal fluid biomarkers, and genetic features were first preprocessed by performing age correction, feature selection, and feature reduction. Then, they were individually scored using LDA, and the scores that represent the AD pathological progress in different modalities were obtained. Finally, an extreme learning machine-based decision tree was established to perform multiclass diagnosis using these scores. The experiments were conducted on the AD Neuroimaging Initiative dataset, and accuracies of 66.7% and 57.3% and F1-scores of 64.9% and 55.7% were achieved in three- and four-way classifications, respectively. The results also showed that the proposed framework achieved a better performance than the method that did not score multimodal data and the methods in previous studies, thereby indicating that the LDA scoring strategy is an efficient way for multimodalities fusion in AD multiclass classification. •The linear discriminant analysis scoring method for multimodal data fusion can significantly improve the performance.•A binary extreme learning machine based tree decision strategy was used for Alzheimer's disease multiclass diagnosis.•The proposed method improved the performance in terms of recall, precision, F1-score, and accuracy.•Obtained accuracy of 66.7% and 57.3% in three-way and four-way classifications.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2021.104478