Abstract
Analysis of data based on gene expressions characterizing serious disease is an area currently receiving high attention. The basic task is to classify patients, usually by searching for a small group of genes that provides sufficient classification power. However, very often, different gene combinations can describe different aspects of the problem being analyzed. In this paper, we present in a concrete example with one real dataset, a methodology that has repeatedly been successfully applied to different types of data. In addition to common statistical methods, this methodology combines methods such as a visualization of a dataset structure using networks, and feature-selection and neural network classification. The output of the application of the methodology is a system for decision support during the reoperation of patients with joint endoprosthesis.
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References
Liu, B., Cui, Q., Jiang, T., Ma, S.: A combinational feature selection and ensemble neural network method for classification of gene expression data. BMC Bioinform. 5(1), 136 (2004)
Moteghaed, N.Y., Maghooli, K., Pirhadi, S., Garshasbi, M.: Biomarker discovery based on hybrid optimization algorithm and artificial neural networks on microarray data for cancer classification. J. Med. Signals Sens. 5(2), 88 (2015)
Mehridehnavi, A., Ziaei, L.: Minimal gene selection for classification and diagnosis prediction based on gene expression profile. Adv. Biomed. Res. 2 (2013)
Hajian-Tilaki, K.: Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J. Intern. Med. 4(2), 627–635 (2013)
Macskassy, S. Provost, F.: Confidence bands for ROC curves: methods and an empirical study. In: Proceedings of the First Workshop on ROC Analysis in AI, August 2004 (2004)
Zehnalova, S., Kudelka, M., Platos, J., Horak, Z.: Local representatives in weighted networks. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 870–875. IEEE (2014)
Dayhoff, J.E., DeLeo, J.M.: Artificial neural networks: opening the black box. Cancer 91(Suppl. 8), 1615–1635 (2001)
Cipriano, C., Maiti, A., Hale, G., Jiranek, W.: The host response: Toll-like receptor expression in periprosthetic tissues as a biomarker for deep joint infection. J. Bone Jt. Surg. Am. 96(20), 1692–1698 (2014)
Acknowledgments
This work was supported by grant of Ministry of Health of Czech Republic (MZ CR VES16-31852A) and by SGS, VSB-Technical University of Ostrava, under the grant no. SP2017/85.
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Radvansky, M., Kudelka, M., Kriegova, E., Fillerova, R. (2018). Decision Support System in Orthopedics Using Methodology Based on a Combination of Machine Learning Methods. In: Krömer, P., Alba, E., Pan, JS., Snášel, V. (eds) Proceedings of the Fourth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2017. Advances in Intelligent Systems and Computing, vol 682. Springer, Cham. https://doi.org/10.1007/978-3-319-68527-4_21
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DOI: https://doi.org/10.1007/978-3-319-68527-4_21
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