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Classification of Kidney Cancer Data Using Cost-Sensitive Hybrid Deep Learning Approach

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Medical Research Institute, Chungbuk National University, Cheongju 28644, Korea
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College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea
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Department of Nursing, Woosong College, Daejeon 34606, Korea
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Department of Biomedical Engineering, School of Medicine, Chungbuk National University, Cheongju 28644, Korea
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(1), 154; https://doi.org/10.3390/sym12010154 (registering DOI)
Received: 15 December 2019 / Revised: 30 December 2019 / Accepted: 10 January 2020 / Published: 11 January 2020
Recently, large-scale bioinformatics and genomic data have been generated using advanced biotechnology methods, thus increasing the importance of analyzing such data. Numerous data mining methods have been developed to process genomic data in the field of bioinformatics. We extracted significant genes for the prognosis prediction of 1157 patients using gene expression data from patients with kidney cancer. We then proposed an end-to-end, cost-sensitive hybrid deep learning (COST-HDL) approach with a cost-sensitive loss function for classification tasks on imbalanced kidney cancer data. Here, we combined the deep symmetric auto encoder; the decoder is symmetric to the encoder in terms of layer structure, with reconstruction loss for non-linear feature extraction and neural network with balanced classification loss for prognosis prediction to address data imbalance problems. Combined clinical data from patients with kidney cancer and gene data were used to determine the optimal classification model and estimate classification accuracy by sample type, primary diagnosis, tumor stage, and vital status as risk factors representing the state of patients. Experimental results showed that the COST-HDL approach was more efficient with gene expression data for kidney cancer prognosis than other conventional machine learning and data mining techniques. These results could be applied to extract features from gene biomarkers for prognosis prediction of kidney cancer and prevention and early diagnosis. View Full-Text
Keywords: data mining; machine learning; kidney cancer; bioinformatics; autoencoder; neural network; cost-sensitive; hybrid deep learning; cancer classification data mining; machine learning; kidney cancer; bioinformatics; autoencoder; neural network; cost-sensitive; hybrid deep learning; cancer classification
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Shon, H.S.; Batbaatar, E.; Kim, K.O.; Cha, E.J.; Kim, K.-A. Classification of Kidney Cancer Data Using Cost-Sensitive Hybrid Deep Learning Approach. Symmetry 2020, 12, 154.

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