Deep Neural Networks for Optimal Selection of Features Related to Flu

B Tarakeswara Rao; V N Lakshmana Kumar; D Padmapriya; Kumud Pant; Tejaswini B; Wadi B Alonazi; Khalid M A Almutairi; D Raj; Ramesh Shahabadkar

doi:10.1155/2022/7639875

Deep Neural Networks for Optimal Selection of Features Related to Flu

Evid Based Complement Alternat Med. 2022 Jul 14:2022:7639875. doi: 10.1155/2022/7639875. eCollection 2022.

Authors

B Tarakeswara Rao¹, V N Lakshmana Kumar², D Padmapriya³, Kumud Pant⁴, Tejaswini B⁵, Wadi B Alonazi⁶, Khalid M A Almutairi⁷, D Raj⁸, Ramesh Shahabadkar⁹

Affiliations

¹ Department of Computer Science & Engineering, Kallam Haranadhareddy Institute of Technology, Dasaripalem, Andhra Pradesh 522019, India.
² Department of Electronics and Communication Engineering, M.V.G.R.College of Engineering (Autonomous), Vizianagaram, Andhra Pradesh 535005, India.
³ Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Tamil Nadu 600123, India.
⁴ Department of Biotechnology, Graphic Era Deemed to Be University, Dehradun, Uttarakhand 248002, India.
⁵ Department of Information Science and Engineering, East Point College of Engineering and Technology, Bengaluru, Karnataka 560049, India.
⁶ Health Administration Department, College of Business Administration, King Saud University, P.O. Box. 71115, Riyadh 11587, Saudi Arabia.
⁷ Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, P.O. Box. 10219, Riyadh 11433, Saudi Arabia.
⁸ Zoonosis Research Center, School of Medicine, Wonkwang University, Iksan, Republic of Korea.
⁹ Department of Electrical and Computer Engineering, Ambo University, Woliso Campus, Waliso, Ethiopia.

Abstract

In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu.

Publication types

Retracted Publication