Performance Evaluation and Optimization of Mathematical Optimization Algorithms in Large-scale Data Processing and Analysis

With the booming rise of the big data industry and the widespread adoption of smart healthcare concepts, the healthcare industry is undergoing unprecedented changes. Massive medical datasets are widely used to assist medical diagnosis, and their quality has become a key factor directly affecting diagnostic accuracy and efficiency. In this context, how to efficiently and accurately process and analyze these large-scale data has become an important issue that urgently needs to be addressed. This article delves into the performance and optimization strategies of mathematical optimization algorithms in handling large-scale medical data processing and analysis tasks. This paper proposes a medical data processing algorithm based on machine learning (ML) to address the problems of low computational efficiency and limited accuracy faced by traditional methods in processing complex and high-dimensional data. This algorithm effectively improves the speed and accuracy of data processing by integrating advanced ML models and algorithm optimization techniques, providing strong support for achieving precision medicine. The experimental results show that the algorithm not only significantly improves the efficiency of data processing when dealing with large-scale medical datasets, but also further optimizes the accuracy of diagnostic results.