ArticleMedical & biological engineering & computing2013
Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients.
Article in Medical & biological engineering & computing, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- Review
- Prediction and Detection of Cervical Malignancy Using Machine Learning Models.Asian Pacific journal of cancer prevention : APJCP · 2023Article
- Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer.Journal of biomedical physics & engineering · 2020Article
- Biometric Identification from Human Aesthetic Preferences.Sensors (Basel, Switzerland) · 2020Article
- Prediction of essential genes in prokaryote based on artificial neural network.Genes & genomics · 2020Article
- A novel gene selection algorithm for cancer classification using microarray datasets.BMC medical genomics · 2019Article
- Prediction of 10-year Overall Survival in Patients with Operable Cervical Cancer using a Probabilistic Neural Network.Journal of Cancer · 2019Article
- An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming.Medical & biological engineering & computing · 2018Article
- Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods.BMC cancer · 2017Article
- Prediction of NSCLC recurrence from microarray data with GEP.IET systems biology · 2017Article
- The prediction of virus mutation using neural networks and rough set techniques.EURASIP journal on bioinformatics & systems biology · 2016Article
- Lung cancer prediction from microarray data by gene expression programming.IET systems biology · 2016Article
- Identifying relevant group of miRNAs in cancer using fuzzy mutual information.Medical & biological engineering & computing · 2016Article
- A Highly Efficient Gene Expression Programming (GEP) Model for Auxiliary Diagnosis of Small Cell Lung Cancer.PloS one · 2015Article
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3 authors.
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Abstract
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.
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