Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients

Kusy, Maciej; Obrzut, Bogdan; Kluska, Jacek

doi:10.1007/s11517-013-1108-8

Cited by 52 publications

(25 citation statements)

References 52 publications

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“…GEP has been used to predict adverse events of radical hysterectomy in cervical cancer patients (Kusy et al, 2013). Moreover, a GEP was applied to automatically detect the population with fatty liver in computed the teat date of serum glucose, the total cholesterol, low density lipoprotein cholesterol, high density lipoprotein cholesterol and triglyceride from 196 power plant workers.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

Chen²,

Cui³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool -gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are requentlyused lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer.

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Section: Introductionmentioning

confidence: 99%

Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

Chen²,

Cui³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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“…The methods and techniques have been used for www.ijacsa.thesai.org screening of cervical cancer are limited to small number of parameters. The available literature for screening of cervical cancer explores mainly Papanicolaou (Pap) smear test [17], hormonal status, FIGO stage [18] and cervical intraepithelial neoplasia (CIN) [19] but only single parameter was used for screening prediction of cervical cancer. The available data mining techniques using large number of parameters [20][21][22][23] were not given effective results.…”

Section: Introductionmentioning

confidence: 99%

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Alam¹,

Milhan²,

Atif³

et al. 2019

ijacsa

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Cervical cancer remains an important reason of deaths worldwide because effective access to cervical screening methods is a big challenge. Data mining techniques including decision tree algorithms are used in biomedical research for predictive analysis. The imbalanced dataset was obtained from the dataset archive belongs to the University of California, Irvine. Synthetic Minority Oversampling Technique (SMOTE) has been used to balance the dataset in which the number of instances has increased. The dataset consists of patient age, number of pregnancies, contraceptives usage, smoking patterns and chronological records of sexually transmitted diseases (STDs). Microsoft azure machine learning tool was used for simulation of results. This paper mainly focuses on cervical cancer prediction through different screening methods using data mining techniques like Boosted decision tree, decision forest and decision jungle algorithms as well performance evaluation has done on the basis of AUROC (Area under Receiver operating characteristic) curve, accuracy, specificity and sensitivity. 10-fold cross-validation method was utilized to authenticate the results and Boosted decision tree has given the best results. Boosted decision tree provided very high prediction with 0.978 on AUROC curve while Hinslemann screening method has used. The results obtained by other classifiers were significantly worse than boosted decision tree.

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“…They mimic the decision-making abilities of a human expert, and they are designed to solve complex problems by reasoning. Expert system applications include, among others, medicine [29,53,60], diagnosis and control of power systems [26,27], evaluation of journal grades [61], information systems investment evaluation [19], transport management [31,51], industry [14,38] and sport [12,15,32,33,39].…”

Section: Introductionmentioning

confidence: 99%

A web-oriented expert system for planning hurdles race training programmes

Przednówek

Wiktorowicz

Krzeszowski

et al. 2018

Neural Comput & Applic

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This paper presents a web-oriented expert system, named iHurdling, to predict results and generate training loads for 110 and 400 m hurdles races. The database contains 40 annual training programmes for the 110 m hurdles and 48 programmes for the 400 m hurdles. The predictive models include linear regressions in the form of ordinary least squares, ridge, LASSO, elastic net and nonlinear models in the form of a radial basis function neural network and fuzzy rule-based system. The leave-one-out cross-validation method is used to compare, and choose the best model. It shows that the proposed fuzzy-based model has the lowest validation error. The developed web application can support a coach in planning training programmes for hurdles races. It allows the athlete's results to be predicted and can generate training loads for an athlete, selected from database. The application can be run on a computer or a mobile device. The system was implemented using the R programming language with the Shiny framework and additional packages. The limitations of the presented approach are related to the lack of consideration of an athlete's physiological and psychological parameters, but the generated training programs might be used as a suggestion for the coach.

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Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients

Cited by 52 publications

References 52 publications

Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

Cervical Cancer Prediction through Different Screening Methods using Data Mining

A web-oriented expert system for planning hurdles race training programmes

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