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

Shon, Ho Sun; Batbaatar, Erdenebileg; Kim, Kyoung Ok; Jong, Eun; Kim, Kyung-Ah

doi:10.3390/sym12010154

Cited by 31 publications

(16 citation statements)

References 28 publications

(8 reference statements)

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“…Piao et al [22], proposed an ensemble approach for predicting prostate cancer RNA-Seq data, by simulating nonparametric methods and submitted its application on other diseases. Shon et al [23], proposed a classification approach using an endways, cost-effective hybrid deep-learning method on a combined clinical kidney cancer gene data, by combining deep symmetric auto-encoder. They determined the optimal and estimated classification accuracy model, the experiment showed a better efficiency compared to hi-tech and can be functional for extracting features from a gene biomarker for prognosis, diagnosis and prevention of kidney cancer.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Heuristic Dimensionality Reduction Methods for Classifying Malaria Vector Gene Expression Data

Arowolo

Adebiyi

et al. 2020

IEEE Access

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Malaria is the world's leading cause of death, spread by Anopheles mosquitoes. Gene expression is a fundamental level where the effects of unseen vital revealing genes and developmental systems can be evident for detection of distinctions in malaria infections, to recognize the biological processes in human. Ribonucleic acid sequencing offers a large-scale measurable generated profiling transcriptional data results that help a variety of applications such as scientific and clinical condition studies. A fundamental limitation of ribonucleic acid sequencing consists of high dimensional, infrequent and noises, making classification of genes challenging. Several approaches have proposed enhancing the problem of the curse of dimensionality problem, requiring more improvement, yet it is critical to obtain accurate results. In this study, a hybrid dimensionality reduction technique proposes an optimized Genetic algorithm to pick pertinent subset features from the data. Features chosen is passed into principal component analysis and independent component analysis methods grounded on their class variants, to help transform the selected elements into a lower dimension separately. Support vector machine kernel classifiers used the reduced malaria vector dataset to assess the classification performance of the experiment.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Heuristic Dimensionality Reduction Methods for Classifying Malaria Vector Gene Expression Data

Arowolo

Adebiyi

et al. 2020

IEEE Access

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“…Another way to deal with the class imbalance problem has been through the Cost Sensitive (CS) approach [34], which has become an important topic in deep learning research in recent years [13][14][15]35]. CS considers the costs associated with misclassifying samples; i.e., it uses different cost matrices describing the costs of misclassifying any particular data sample [29].…”

Section: Introductionmentioning

confidence: 99%

Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem

et al. 2020

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The class imbalance problem has been a hot topic in the machine learning community in recent years. Nowadays, in the time of big data and deep learning, this problem remains in force. Much work has been performed to deal to the class imbalance problem, the random sampling methods (over and under sampling) being the most widely employed approaches. Moreover, sophisticated sampling methods have been developed, including the Synthetic Minority Over-sampling Technique (SMOTE), and also they have been combined with cleaning techniques such as Editing Nearest Neighbor or Tomek's Links (SMOTE+ENN and SMOTE+TL, respectively). In the big data context, it is noticeable that the class imbalance problem has been addressed by adaptation of traditional techniques, relatively ignoring intelligent approaches. Thus, the capabilities and possibilities of heuristic sampling methods on deep learning neural networks in big data domain are analyzed in this work, and the cleaning strategies are particularly analyzed. This study is developed on big data, multi-class imbalanced datasets obtained from hyper-spectral remote sensing images. The effectiveness of a hybrid approach on these datasets is analyzed, in which the dataset is cleaned by SMOTE followed by the training of an Artificial Neural Network (ANN) with those data, while the neural network output noise is processed with ENN to eliminate output noise; after that, the ANN is trained again with the resultant dataset. Obtained results suggest that best classification outcome is achieved when the cleaning strategies are applied on an ANN output instead of input feature space only. Consequently, the need to consider the classifier's nature when the classical class imbalance approaches are adapted in deep learning and big data scenarios is clear.

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“…It was also used for diagnosing Parkinson disease [59] , [60] . Additionally, AEN was used for diagnosing osteoporosis disease [61] , type 2 diabetes [62] , prostate [63] , brain [64] (as being recognition related), and even different cancer types [65] , [66] , [67] .…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network

Deperlioğlu

Köse

Gupta

et al. 2020

Computer Communications

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Objective of this study is to introduce a secure IoHT system, which acts as a clinical decision support system with the diagnosis of cardiovascular diseases. In this sense, it was emphasized that the accuracy rate of diagnosis (classification) can be improved via deep learning algorithms, by needing no hybrid-complex models, and a secure data processing can be achieved with a multi-authentication and Tangle based approach. In detail, heart sounds were classified with Autoencoder Neural Networks (AEN) and the IoHT system was built for supporting doctors in real-time. For developing the diagnosis infrastructure by the AEN, PASCAL B-Training and Physiobank-PhysioNet A-Training heart sound datasets were used accordingly. For the PASCAL dataset, the AEN provided a diagnosis-classification performance with the accuracy of 100%, sensitivity of 100%, and the specificity of 100% whereas the rates were respectively 99.8%, 99.65%, and 99.13% for the PhysioNet dataset. It was seen that the findings by the developed AEN based solution were better than the alternative solutions from the literature. Additionally, usability of the whole IoHT system was found positive by the doctors, and according to the 479 real-case applications, the system was able to achieve accuracy rates of 96.03% for normal heart sounds, 91.91% for extrasystole, and 90.11% for murmur. In terms of security approach, the system was also robust against several attacking methods including synthetic data impute as well as trying to penetrating to the system via central system or mobile devices.

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

Cited by 31 publications

References 28 publications

A Hybrid Heuristic Dimensionality Reduction Methods for Classifying Malaria Vector Gene Expression Data

A Hybrid Heuristic Dimensionality Reduction Methods for Classifying Malaria Vector Gene Expression Data

Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem

Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network

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