Robust Adverse Drug Reaction Prediction and Classification by Employing Deer Hunting Optimization Driven Deep Learning Approach

Drugs for medical purpose aims to save a person's life and improve the quality of life. They enhance one's mental or physical fitness and treat disease. However, drugs might cause adverse reactions or even unexpected effects on patients, known as Adverse Drug Reactions (ADRs). For preventing ADRs, drug trials were carried out to evaluate and analyze the potential danger in the procedure of drug development. Deep learning (DL) is a type of Machine Learning (ML) in Artificial intelligence (AI) that has developed as a highly effective and promising method that can interrogate and combine diverse biological data kinds to produce new hypotheses. DL is extensively used in drug repurposing and discovery, but its application in ADR prediction employing gene expression data is limited. Therefore, this study introduces a Deer Hunting Optimization Driven Deep Learning Model for Robust Adverse Drug Reaction Recognition and Classification (DHODL-ADRRC) technique. The DHODL-ADRRC technique involves diverse phases of data preprocessing to normalize the data. In addition, the Binary Fire Hawks Optimization (BFHO) based feature selection (FS) method is used to elect an optimum set of features. Moreover, the Attention-based Convolutional Bidirectional Long Short-Term Memory (ACBLSTM) algorithm is utilized for ADR identification. Furthermore, the DHO model is exploited for the tuning process adjusting of the ACBLSTM model, advancing the classification outputs. The simulation output of the DHODL-ADRRC algorithm was investigated on the ADR dataset, and the extensive outputs highlighted the advanced achievement of the DHODL-ADRRC method over other current methods in terms of different measures.