2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 16 , Issue 1 , PP: 75-85, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
N. Deepaletchumi 1 * , R. Mala 2
Doi: https://doi.org/10.54216/JISIoT.160107
Adverse Drug Reactions (ADRs) are very hazardous to patients. Thus, the detection of ADR intends to automatically distinguish, which is an intensive study for public health monitoring functions. Detecting ADRs is the most significant information to determine the patient’s opinion on some drugs. As patients can experience projected and occasionally unpredicted negative results from taking some drugs, late detection of ADRs may place life-threatening dangers to patients; posing significant financial, social, and legal consequences to the regulatory agencies and manufacturing companies. The usage of medical data, like states and electronic health records (EHR), became normal in offering a richer understanding of health services and assisting ADR analysis. Developments in deep learning (DL) and machine learning (ML) have made several analytic models have the potential to apply higher-dimensional data to predict adverse effects. In this study, we present a Hippopotamus Optimizer-Based Feature Selection for Adverse Drug Reaction Detection Using a Variational Autoencoder (HOFS-ADRDVAE) model. The main intention of the HOFS-ADRDVAE model is to provide an automatic system for the detection of ADR using state-of-the-art techniques. Initially, the data normalization stage employs min-max normalization for converting input data into a beneficial format. In addition, the feature selection process has been executed by the hippopotamus optimization (HO) algorithm. Besides, the proposed HOFS-ADRDVAE model designs a variational autoencoder (VAE) technique for the classification procedure. At last, the Hunger Games search (HGS) algorithm-based hyperparameter selection process is executed to optimize the classification results of the VAE system. A wide-ranging experiment was implemented to point out the performance of the HOFS-ADRDVAE method. The experimental outcomes specified that the HOFS-ADRDVAE model emphasized improvement over another existing method.
Hippopotamus Optimizer , Feature Selection , Adverse Drug Reaction Detection , Variational Autoencoder , Hyperparameter Tuning
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