Rider-chicken optimization dependent recurrent neural network for cancer detection and classification using gene expression data

ABSTRACT

One of the deadly diseases prevailing worldwide is cancer. The rigorous symptoms of cancers should be studied properly prior to the diagnosis to save patients life. Thus, an automatic prediction system for classifying cancer using gene expression data is needed. This paper develops a cancer classification and detection method by proposing the Rider Chicken Optimisation algorithm dependent Recurrent Neural Network (RCO-RNN) classifier. At first, pre-processing is done on the gene expression data to fit for the further processes of classification. In gene selection, the genes are selected based on entropy for reducing the dimension. Finally, the selected genes are classified using Recurrent Neural Network (RNN), which is trained by using the proposed Rider Chicken Optimisation (RCO) algorithm, which is the integration of Chicken Swarm Optimisation (CSO), and Rider Optimisation algorithm (ROA). The experimentation is carried out using the Leukaemia database, Small Blue Round Cell Tumour (SBRCT) dataset and Lung Cancer Dataset. The performance of the RCO-RNN is evaluated based on specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. The proposed method produces the maximal accuracy, sensitivity, PPV, NPV and specificty upto 95%. Which indicates the superiority of the proposed method.

KEYWORDS:

• Cancer classification
• recurrent neural network
• Rider optimisation algorithm
• chicken swarm optimisation
• gene expression data

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Chetan Nimba Aher

Chetan Nimba Aher is currently working as an Assistant Professor at All India Shri Shivaji Memorial Society's Institute of Information Technology, Pune, Maharashtra, India. He received M.E. In Computer Engineering from Savitribai Phule Pune University in 2016. Currently he is pursuing his Ph. D. in Computer Science and Engineering from KIIT Deemed to be University, Bhubaneshwar, Odhisha, India. His area of interests are Soft Computing, Artificial Intelligence and Data Mining. He has published more than 7 papers in international journals and conferences.

Ajay Kumar Jena

Ajay Kumar Jena is currently working as an assistant professor at School of Computer Engineering, KIIT Deemed to be University, Bhubaneshwar, Odhisha, India. He received his Ph. D. in Software Engineering from KIIT Deemed to be University in 2016. His main teaching areas are Software Engineering, Soft Computing, Programming Languages, Operating Systems, Data Mining and Cryptography. He has published more than 15 papers in papers in international journals and conference proceedings, and two book chapters. He has also edited one book of international repute. He is an editorial/reviewer board member for various journals and conferences.