Enhancing Adverse Drug Reaction Classification of Attention Deficit Hyperactivity Disorder Diagnosis Data Using Deep Learning with Optimization Algorithm

Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3572 2018 2018 Enhancing Adverse Drug Reaction Classification of Attention Deficit Hyperactivity Disorder Diagnosis Data Using Deep Learning with Optimization Algorithm Research Scholar, Department of Computer Applications, Alagappa University, Karaikudi, India N. N. Asst.Prof & Head, Department of Computer Science, Government Arts and Science College for Women, Paramakudi, India R. Mala Adverse Drug Reaction (ADR) is a significant global public health issue and the main cause of death. Generally, the effects of ADR are complex. Clinically, they can cause major patient damage and, in some cases, death. Besides, this outcome in significant healthcare costs financially owing to enlarged hospital visits, extra treatments, and harm to productivity. Therefore, early recognition and mitigation of ADRs are vital for the patients. Enhancing the early detection of ADRs and deadliness could severely reduce the harm to patients, improve patient safety, decrease healthcare costs, and increase the efficacy of the drug development procedure. Conventional pre-clinical toxicity tests are expensive, time-consuming, and frequently fail to forecast human-specific toxic effects. Artificial Intelligence (AI)-based deep learning (DL) has been quickly adopted in numerous areas, with healthcare, for its latent to manage huge datasets, find out patterns, and generate predictions. This study presents a new Adverse Drug Reaction Detection through Deep Learning and Improved Red-Tailed Hawk Algorithm (ADRD-DLIRTHA). The main intention of the ADRD-DLIRTHA model is to enhance the detection and classification process of ADR using advanced hybrid and optimization techniques. At first, the data normalization stage applies z-score normalization for converting input data into a beneficial set-up. Furthermore, the proposed ADRD-DLIRTHA method designs a convolutional neural network and long short-term memory (CNN-LSTM) technique for the classification process. At last, the improved red-tailed hawk (IRTH) algorithm-based hyperparameter selection process has been applied to optimize the classification results of the CNN-LSTM system. A wide range of experimentation was led to authorize the performance of the ADRD-DLIRTHA system. The simulation results specified that the ADRD-DLIRTHA model emphasized advancement over other existing techniques 2025 2025 38 49 10.54216/FPA.190104 https://www.americaspg.com/articleinfo/3/show/3572