Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients.
The aim of this article was to compare gene expression programming (GEP) method with three types of neural networks in the prediction of adverse events of radical hysterectomy in cervical cancer patients. One-hundred and seven patients treated by radical hysterectomy were analyzed. Each record repre...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 12; pp. 1357 - 1366 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Dec2013
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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=104111852&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104111852 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2013 vid: 51 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104111852 NLM24136688 2012362867 10.1007/s11517-013-1108-8 NLM24136688 PMC3825140 104111852 ppf: 1357 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients. aug: au: Kusy, Maciej Obrzut, Bogdan Kluska, Jacek affil: Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, W. Pola 2, 35-959, Rzeszow, Poland, mkusy@prz.edu.pl. sug: subj: Hysterectomy Adverse Effects Models, Statistical Neural Networks (Computer) Cervix Neoplasms Cervix Neoplasms Surgery Adult Aged Algorithms Bioinformatics Methods Computer Simulation Female Gene Expression Profiling Human Hysterectomy Statistics and Numerical Data Middle Age Postoperative Complications Etiology Predictive Value of Tests Prospective Studies ROC Curve Treatment Outcomes Cervix Neoplasms Metabolism Cervix Neoplasms Pathology Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female ab: The aim of this article was to compare gene expression programming (GEP) method with three types of neural networks in the prediction of adverse events of radical hysterectomy in cervical cancer patients. One-hundred and seven patients treated by radical hysterectomy were analyzed. Each record representing a single patient consisted of 10 parameters. The occurrence and lack of perioperative complications imposed a two-class classification problem. In the simulations, GEP algorithm was compared to a multilayer perceptron (MLP), a radial basis function network neural, and a probabilistic neural network. The generalization ability of the models was assessed on the basis of their accuracy, the sensitivity, the specificity, and the area under the receiver operating characteristic curve (AUROC). The GEP classifier provided best results in the prediction of the adverse events with the accuracy of 71.96 %. Comparable but slightly worse outcomes were obtained using MLP, i.e., 71.87 %. For each of measured indices: accuracy, sensitivity, specificity, and the AUROC, the standard deviation was the smallest for the models generated by GEP classifier. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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