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

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.