Affiliations: [a] Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| [b] Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, Taiyuan, China
Correspondence:
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Correspondence to: Hongmei Yu, Department of Health Statistics, School of Public Health, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, P.R. China. Tel.: +86 351 4135049; Fax: +86 351 2027943; E-mail: [email protected].
Note: [1] Data used in this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation ofADNIand/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/howtoapply/ADNIAcknowledgementList.pdf.
Abstract: Background:Alzheimer’s disease (AD) is a severe health problem. Challenges still remain in early diagnosis. Objective:The objective of this study was to build a Stacking framework for multi-classification of AD by a combination of neuroimaging and clinical features to improve the performance. Methods:The data we used were from the Alzheimer’s Disease Neuroimaging Initiative database with a total of 493 subjects, including 125 normal control (NC), 121 early mild cognitive impairment, 109 late mild cognitive impairment (LMCI), and 138 AD. We selected structural magnetic resonance imaging (sMRI) feature by voting strategy. The imaging feature, demographic information, Mini-Mental State Examination, and Alzheimer’s Disease Assessment Scale-Cognitive Subscale were combined together as classification features. We proposed a two-layer Stacking ensemble framework to classify four types of people. The first layer represented support vector machine, random forest, adaptive boosting, and gradient boosting decision tree; the second layer was a logistic regression classifier. Additionally, we analyzed performance of only sMRI feature and combined features and compared the proposed model with four base classifiers. Results:The Stacking model combined with sMRI and non-imaging features outshined four base classifiers with an average accuracy of 86.96%. Compared with using sMRI data alone, sMRI combined with non-imaging features significantly improved diagnostic accuracy, especially in NC versus LMCI. Conclusion:The Stacking framework we used can improve performance in diagnosis of AD using combined features.
