Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis.
Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples, thus causing low prediction accuracy. Hence, it...
| Publicado en: | Journal of Biomedical Informatics Vol. 92; pp. 103124 - 103125 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Apr2019
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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=136179795&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136179795 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Apr2019 vid: 92 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 136179795 136179795 NLM30796977 136179795 10.1016/j.jbi.2019.103124 NLM30796977 136179795 ppf: 103124 ppct: 1 formats: tig: atl: Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis. aug: au: Zhao, Dandan Liu, Hong Zheng, Yuanjie He, Yanlin Lu, Dianjie Lyu, Chen Lv, Chen affil: School of Information Science and Engineering, Shandong Normal University, Jinan City, China sug: subj: Colorectal Neoplasms Diagnosis Gene Expression Profiling Methods Algorithms Colorectal Neoplasms Metabolism Colorectal Neoplasms Gene Expression Profiling Software Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Arthritis Impact Measurement Scales Clinical Assessment Tools ab: Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples, thus causing low prediction accuracy. Hence, it is of vital significance to build proper models that can avoid the above problems and predict the CRC more accurately. In this paper, we use an ensemble model to classify samples into healthy and CRC groups and improve prediction performance. The proposed model is composed of three functional modules. The first module mainly performs the function of removing redundant genes. The main feature genes are selected using minimum redundancy maximum relevance (mRMR) method to reduce the dimensionality of features thereby increasing the prediction results. The second module aims to solve the problem caused by imbalanced data using hybrid sampling algorithm RUSBoost. The third module focuses on the classification algorithm optimization. We use mixed kernel function (MKF) based support vector machine (SVM) model to classify an unknown sample into healthy individuals and CRC patients, and then, the Whale Optimization Algorithm (WOA) is applied to find most optimal parameters of the proposed MKF-SVM. The final results show that the proposed model achieves higher G-means than other comparable models. The conclusion comes to show that RUSBoost wrapping WOA + MKF-SVM model can be applied to improve the predictive performance of colorectal cancer based on the imbalanced data. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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