Background: Alzheimer's disease (AD) has attracted more and more attention in recent years.
Accurate diagnosis of AD is significant, especially its prodromal stage, i.e., mild cognitive impairment (MCI),
for timely therapy is possibly beneficial to delay the disease progression. Some existing studies indicated that
different biomarkers provide complementary information to discriminate MCI patients from healthy normal
controls (NCs), but the high complexity of these algorithms brought high computational cost.
Objective: To identify Alzheimer's disease in its early stage with a low computational complexity where the
complementary of different biomarkers can still be used.
Method: In this work, we employ the methodology of ensemble learning to construct a two-stage classifier for
combining the classification capacity of three biomarkers, i.e., magnetic resonance imaging (MRI), positron
emission tomography (PET), and quantification of specific proteins measured through cerebrospinal fluid
(CSF), to identify MCIs from healthy controls based on support vector machines algorithm. In the first stage,
two SVM classifiers based on MRI and CSF are used for the identification of MCI, respectively. For the
samples which can get the same results in the first stage will be seen as the training data, and the ones with the
inconsistent result will be put into the second stage as the test data, where PET features are adapted to the
Results: An original dataset downloaded from ADNI database, where 99 MCI patients and 52 healthy controls
included, had been adopted for the validation of our proposed method. The experimental results demonstrated
the effectiveness of the two-stage ensemble classifier with a classification accuracy of 75.5%, a sensitivity of
78.4% and a specificity of 70.0%.
Conclusion: This study proposed a computational framework to identify the early stage of AD by a two-stage
ensemble strategy. The performance of this work shows that combination of different biomarkers can improve
identification of MCI with a relatively low computational cost, which is very meaningful for the diagnosis and
delay of the AD progress.