Welcome to the InfoSci Platform
Reference Hub1
Hybrid Optimization-Based Structural Design of Deep Q Network With Feature Selection Algorithm for Medical Data Classification

Hybrid Optimization-Based Structural Design of Deep Q Network With Feature Selection Algorithm for Medical Data Classification

Radhanath Patra, Bonomali Khuntia, Dhruba Charan Panda
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 20
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781683181514|DOI: 10.4018/IJSIR.304722
Cite Article Cite Article

MLA

Patra, Radhanath, et al. "Hybrid Optimization-Based Structural Design of Deep Q Network With Feature Selection Algorithm for Medical Data Classification." IJSIR vol.13, no.1 2022: pp.1-20. http://doi.org/10.4018/IJSIR.304722

APA

Patra, R., Khuntia, B., & Panda, D. C. (2022). Hybrid Optimization-Based Structural Design of Deep Q Network With Feature Selection Algorithm for Medical Data Classification. International Journal of Swarm Intelligence Research (IJSIR), 13(1), 1-20. http://doi.org/10.4018/IJSIR.304722

Chicago

Patra, Radhanath, Bonomali Khuntia, and Dhruba Charan Panda. "Hybrid Optimization-Based Structural Design of Deep Q Network With Feature Selection Algorithm for Medical Data Classification," International Journal of Swarm Intelligence Research (IJSIR) 13, no.1: 1-20. http://doi.org/10.4018/IJSIR.304722

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

In the area of medical informatics, the medical data classification is considered a complicated job. However, accurate classification of medical data is a complex task. Therefore, a robust and effective hybrid optimization-based deep learning method for classifying the medical data is developed in this research. The input data is pre-processed using data normalization method. Then, the features are selected using the proposed Henry Sea Lion Optimization (HSLnO), which is the combination of Henry Gas Solubility Optimization (HGSO) and Sea Lion Optimization (SLnO). The classification process is achieved using an optimized Deep Q Network (DQN). The DQN is optimized using the proposed Shuffled Shepherd Whale optimization Algorithm (SSWOA). The proposed SSWOA is developed by the integration of the Shuffled Shepherd Optimization Algorithm (SSOA) and Whale Optimization Algorithm (WOA). The developed technique achieves better performance of testing accuracy, sensitivity, and specificity with values of 95.413%, 95.645%, and 95.364%, respectively.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.