Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data.
Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a s...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 4; pp. 709 - 721 |
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| Autores principales: | , , , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2018
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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=128549172&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128549172 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2018 vid: 56 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 128549172 128549172 NLM28891000 128549172 10.1007/s11517-017-1722-y NLM28891000 128549172 ppf: 709 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data. aug: au: Chatterjee, Sankhadeep Dey, Nilanjan Shi, Fuqian Ashour, Amira S. Fong, Simon James Sen, Soumya affil: Department of Computer Science & Engineering, University of Calcutta, Kolkata, India sug: subj: Dengue Dengue Classification Gene Expression Profiling Methods Dengue Metabolism Bioinformatics Methods Dengue Diagnosis Algorithms Neural Networks (Computer) Diagnosis, Computer Assisted Scales ab: Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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