To achieve the classification of Alzheimer’s disease (AD) by integrating information that utilize the complementary properties of multimodal data, and to provide references for clinical diagnosis.

A total of 872 subjects were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), with both clinical information, structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) scans. They were divided into the training set (612 subjects) and test set (260 subjects). Based on three unimodal data and four multimodal combinations of different modalities, we constructed the sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) classification models in the training set to achieve the multi-classification. The macro-averaged precision (Macro-P), macro-averaged recall (Macro-R), macro-averaged F1 value (Macro-F1), and accuracy were used to evaluate the model performance, with the optimal combination of modalities obtained explored for their applicability in the test set.

The classification performance of clinical information (Macro-P=0.781 8, Macro-R=0.804 6, Macro-F1=0.791 2, Accuracy=0.796 7) among the unimodal information was better than that of sMRI and fMRI modalities. The optimal number of potential components was 1, and the number of clinical information features was 9. Among the four multimodal combinations, the clinical information + fMRI combination had the strongest classification ability (Macro-P=0.806 2,Macro-R=0.800 6, Macro-F1=0.797 6, Accuracy=0.813 2), with the optimal number of potential components selected as 1,and the number of features were 5, while the sMRI + fMRI had the worst classification ability (Macro-P=0.401 7, Macro-R=0.398 3, Macro-F1=0.349 9, Accuracy=0.565 9).Applying the best modal combination to the test set, the model performance metrics achieved were 0.791 8 for Macro-P, 0.734 5 for Macro-R, 0.758 4 for Macro-F1, and 0.766 4(0.646 0, 0.846 5) for Accuracy.

The performance of the sPLS-DA classification model constructed based on each multimodal combination was higher than that of the unimodal, among which the combination of clinical information + fMRI modality had the best performance, which couldgreatly facilitate the formulation of scientific and reasonable clinical diagnosis plans.