Abstract
In the current era of information, the rise of big data technology has become a key force driving the development of the medical and health field. Statistical methods, as core tools for data analysis, have demonstrated unique advantages in processing vast medical datasets. This paper comprehensively analyzes the various applications of big data statistical methods in the medical and health field, focusing particularly on their practical effects and potential value in disease prediction, epidemiological research, medical resource optimization, and personalized medical services. Through a review of relevant literature and in-depth discussions of multiple cases, this paper reveals how statistical methods play a crucial role in improving diagnostic accuracy and medical service efficiency. It also addresses the challenges faced in actual applications, such as data privacy protection and technical standardization.
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