Deep ensemble learning for Alzheimer's disease classification.
Ensemble learning uses multiple algorithms to obtain better predictive performance than any single one of its constituent algorithms could. With the growing popularity of deep learning technologies, researchers have started to ensemble these technologies for various purposes. Few, if any, however, h...
| Publicado en: | Journal of Biomedical Informatics Vol. 105 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143235054&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143235054 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: May2020 vid: 105 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 143235054 143235054 NLM32234546 143235054 10.1016/j.jbi.2020.103411 NLM32234546 143235054 ppct: 1 formats: tig: atl: Deep ensemble learning for Alzheimer's disease classification. aug: au: An, Ning Ding, Huitong Yang, Jiaoyun Au, Rhoda Ang, Ting F.A. affil: Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, Hefei University of Technology, Hefei, China sug: subj: Alzheimer's Disease Diagnosis Algorithms Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Funding Source ab: Ensemble learning uses multiple algorithms to obtain better predictive performance than any single one of its constituent algorithms could. With the growing popularity of deep learning technologies, researchers have started to ensemble these technologies for various purposes. Few, if any, however, have used the deep learning approach as a means to ensemble Alzheimer's disease classification algorithms. This paper presents a deep ensemble learning framework that aims to harness deep learning algorithms to integrate multisource data and tap the 'wisdom of experts'. At the voting layer, two sparse autoencoders are trained for feature learning to reduce the correlation of attributes and diversify the base classifiers ultimately. At the stacking layer, a nonlinear feature-weighted method based on a deep belief network is proposed to rank the base classifiers, which may violate the conditional independence. The neural network is used as a meta classifier. At the optimizing layer, over-sampling and threshold-moving are used to cope with the cost-sensitive problem. Optimized predictions are obtained based on an ensemble of probabilistic predictions by similarity calculation. The proposed deep ensemble learning framework is used for Alzheimer's disease classification. Experiments with the clinical dataset from National Alzheimer's Coordinating Center demonstrate that the classification accuracy of our proposed framework is 4% better than six well-known ensemble approaches, including the standard stacking algorithm as well. Adequate coverage of more accurate diagnostic services can be provided by utilizing the wisdom of averaged physicians. This paper points out a new way to boost the primary care of Alzheimer's disease from the view of machine learning. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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