Material Selection Optimization for Wearable Medical Devices: A Hybrid Approach Using Genetic Algorithms and Multiscale Modeling

Abstract

Abstract: This study presents a new algorithm selection framework for portable medical devices with a genetic algorithm-based approach using multi-scale modeling. This study uses a comprehensive research methodology that includes computer modeling, data visualization, and performance assessment. First, various materials are defined and performance ratings are assigned to set a baseline for the rating. The following data visualizations include bar diagrams, scatter plots, rod diagrams that provide insight into material performance, relationships between cost performance, and convergence of GA. Performance metrics such as accuracy, accuracy, and recall are calculated to measure the effectiveness of the algorithm shown in the bar diagram for subtle evaluations. Additionally, ROC curves (receiver operating characteristics) and confusion matrix are used to assess identification skills and provide a detailed analysis of classification performance. The results demonstrate the knowledge of algorithms in material selection and highlight the importance of accuracy, accuracy and recall in the complex situations of developing WMD. The summary concludes with a summary of the effects of individual visualizations, indicating the potential of the proposed algorithm frame, improving the accuracy and efficiency of the material selection process for portable medical devices. This study contributes to further development of materials science in health care and presents an overall approach to integrating computer technology and data control methods for optimized material selection. Performance evaluation, computer materials science.