Abstract

This study explores the application of big data intelligent algorithms to optimize the operation of flexible biomedical equipment, such as ICU ventilators and wearable monitoring devices. We use machine learning approaches including support vector machines, neural networks, and genetic algorithms to enhance operational efficiency, reduce device downtime, and improve clinical outcomes. By introducing a predictive maintenance and load-balancing model grounded in biomathematical principles, this paper provides a data-driven framework for improving reliability in critical healthcare settings. Objective: The main focus of this study is to examine the application of big data and intelligent algorithms in optimizing flexible equipment operation processes, with the goal of increasing operation efficiency and minimizing cost through the use of smart technology. Methods: This work employs a sophisticated approach that involves data collection from various industrial systems, such as sensors, IoTs, and other control systems. This data is normalized or transformed and checked from applications of machine learning (ML) algorithms such as neural networks (NN) and support vector machines (SVM) and optimization algorithms such as genetic algorithms and particle swarm optimization. To evaluate the effectiveness of these models, the metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared ( ) are used. Results: It was evidenced that the adoption of the proposed methods in managing operations of the flexible equipment was effective in enhancing specific performance metrics. The findings also showed that the accuracy of the proposed NN model is higher in terms of MSE and RMSE values and higher. compared to the support vector machine model. In this regard, optimization algorithms managed to reduce the cycle time by equal to 16.67%, energy consumption by 10%, and maintenance costs by 20%. Future work will include simulating patient-equipment interaction datasets to validate the proposed model using tools such as MATLAB or Python. This will enable visualization of equipment efficiency and failure prediction under varying clinical workloads. Conclusion: Big data and Intelligent Algorithms result in a tremendous improvement in the optimization operations of flexible equipment. These findings reveal the effectiveness of these technologies in boosting predictive efficiency and accuracy. The findings indicated that these techniques could change the way industries operate by increasing flexibility in the processes involved. The proposed intelligent optimization approach enhances the reliability and efficiency of biomedical devices in clinical settings. This work demonstrates the potential of mathematical and computational tools in modeling, predicting, and optimizing equipment operations in healthcare, contributing significantly to the field of biomathematics.