A Deep Neural Network Approach for Cancer Types Classification Using Gene Selection


  • Florence Omada Ocheme Kaduna State University Kaduna State, Nigeria
  • Hakeem Adewale Sulaimon Federal College of Education Zaria, Kaduna State, Nigeria
  • Adamu Abubakar Isah Kaduna State University Kaduna State, Nigeria


Deep Learning, Artificial intelligence Neural Network, Autoencoder, K-Nearest Neighbor, Deep Recurrent Neural Network


Communication in Physical Science, 2021, 7(4): 388-397

Authors: Florence Omada Ocheme, Hakeem Adewale Sulaimon and Adamu Abubakar Isah

Received: 30 November  2021/Accepted 24 December 2021

Cancer classification research is one of the significant areas of exploration in the clinical field. Exact forecasting of various tumor types is an extraordinary challenge and giving an exact forecast will have incredible worth in giving better treatment to the patients. In recent years, many analysis-based investigations have led to the revelation of information on disease subtypes, that has generated high throughput innovations Lately, researchers have attempted to dissect a lot of microarray information for getting significant data that can be utilized in malignancy grouping. To accomplish this objective, one can utilize K-Nearest Neighbor, Neural Networks, Decision Tree, Support Vector a  that would provide approaches needed to break down microarray information towards the choice of best separating quality called biomarker. These machine learning methodologies had the inherent ability to represent the time varying behavior of the underlying biological network that allows for a better representation of spatiotemporal input-output dependencies. Therefore, the exploitation of time series data regarding deep learning has to have become a valuable strategy for deciphering stochastic processes, such as gene expression and classification. Therefore, in this study, another intriguing strategy is introduced to improve the performance of neural networks utilizing deep autoencoder neural networks. This was accomplished through the choice of the first, relevant data, which was being extracted with a Deep Neural Network hidden layer used to train an autoencoder for the classification of the cancer malignancy based on the second stack autoencoder network. The outcome from the proposed experiment was evaluated with the current techniques. Overall, the proposed deep autoencoder accomplished classification accuracy of 99.2% as against the current Modified KNN and SVM which obtained 96.1% and 98.1% respectively


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Author Biographies

Florence Omada Ocheme, Kaduna State University Kaduna State, Nigeria

Department of Computer Science

Hakeem Adewale Sulaimon, Federal College of Education Zaria, Kaduna State, Nigeria

Department of Computer Science

Adamu Abubakar Isah, Kaduna State University Kaduna State, Nigeria

Department of Computer Science


Shukla, A. K., Singh, P. & Vardhan, M. (2020). Gene selection for cancer types classification using novel hybrid metaheuristics approach.. Swarm and Evolutionary Computation, 54, 100661,, doi:10.1016/j.swevo.2020.100661.

Tarek, S., Elwahab, R. A. & Shoman, M. (2017). Gene expression based cancer classification. Egyptian Informatics Journal, 2017. 18(3): p. 151-159.

Halder, A. & Kumar, A. (2019). Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data. Journal of Biomedical Informatics, 2019. 92, 103136, https://doi.org/10.1016/j.jbi.2019.103136

Fix, E. & Hodhes, J. L. (1951). Discriminatory analysis, nonparametric discrimination: consistency properties. Report Number, 4, Project Number 21-49004, UDAF School of Aviation Medicine, Radolph Field, Texas.

Luo, W., Wang, L., & Sun, J. (2009). Feature selection for cancer classification based on support vector machine. Paper presented at the 2009 WRI Global Congress on Intelligent Systems.

Piao, Y., M. Piao, and K.H. Ryu, Multiclass cancer classification using a feature subset-based ensemble from microRNA expression profiles. Computers in biology and medicine, 2017. 80: p. 39-44.

Rani, R.R. & Ramyachitra, D. (2018). Microarray cancer gene feature selection using spider monkey optimization algorithm and cancer classification using SVM. Procedia computer science, 143, pp. 108-116.

Ayyad, S.M., Saleh, A. I. & Labib, L.(2019). Gene expression cancer classification using modified K-Nearest Neighbors technique. BioSystems, 176, pp. 41-51.

Parvin, H., Alizadeh, H. &. Minati, B. (2010). A modification on k-nearest neighbor classifier. Global Journal of Computer Science and Technology, 2010.

Vural, H. & Subaşı, A. (2015). Data-mining techniques to classify microarray gene expression data using gene selection by svd and information gain. Modeling of Artificial Intelligence, 2, pp. 171-182.

Tarek, S., Elwahab, R. A. & Shoman, M. (2016). Cancer classification ensemble system based on gene expression profiles. in 2016 5th International Conference on Electronic Devices Systems and Applications (ICEDSA). 2016. IEEE.

Muthuselvan, S. & Sundaram, S. (2016). Prediction of breast cancer using classification rule mining techniques in blood test datasets. in 2016 International Conference on Information Communication and Embedded Systems (ICICES). 2016. IEEE.

Ting, F. & Sim, K. (2017). Self-regulated multilayer perceptron neural network for breast cancer classification. in 2017 International Conference on Robotics, Automation and Sciences (ICORAS). 2017. IEEE.

Adrian, D. & Annisa, A. (2018). Cancer detection based on microarray data classification with ant colony optimization and modified backpropagation conjugate gradient polak-ribiére. in 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA). 2018. IEEE.

Nawaz, M., Sewissy, A. A. & Soliman, T. H. A. (2018). Multi-class breast cancer classification using deep learning convocational neutral network. Journal of Advanced Computer Science and Application, 9, 6, doi. 10.14569/IJACSA.2018.090645 .

Wu, J. & Hicks, C. (2021). Breast Cancer Type Classification Using Machine Learning. Journal of Perspective Medicine, 11, 2, 61, doi: 10.3390/jpm11020061.