Enhancing Clinical Practice: AI-Driven Personalized Medicine and Evolutionary Strategies for Deep Learning Parameter Optimization

Abstract

This study focuses on the use of improved optimization techniques in deep learning approaches to the determination of personalised medicine. We explore four algorithms: These are; Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Simulated Annealing (SA) to compare the effectiveness on the output models in terms of various parameters. The findings derived from the experimental analysis show that PSO obtained the maximum accuracy of 92%. 5%, precision of 91. Recall is at the lowest with 2% while the remaining is 89%. 7%, which is higher than GA and DE that gained accuracy values of 90. 4% and 91. 0%, respectively. SA while achieving high results proved to have a lower performance compared to others with an accuracy of 88. 3%. The investigation provides a proven fact that PSO outperforms in tuning the deep learning parameters for better and accurate models for the concept of personalized medicine. The above study results imply that the promotion of PSO can improve the development of individualised therapeutic plans, hence benefiting the patients by increasing the probabilities of right diagnoses and corresponding treatment. Thus, this study contributes to the existing literature on AI applications in healthcare by offering insights into the enhancement of deep learning models for improving the overall medical decision-making process.