Research on Hemorrhagic Stroke Based on K-medoids Cluster Analysis and Data Mining

Data mining is an effective computer-aided analysis technology, which can uncover hidden information from large data sets and provide basis for decision-making. At the same time, clustering analysis, as an important branch of data mining, is also widely used in various fields, among which the k-medoids clustering algorithm has better robustness and anti-noise ability than the traditional k-means algorithm. In order to expand the value of these two computer analysis techniques in medical applications, this paper attempts to combine k-medoids cluster analysis and data mining methods to apply them to the analysis and research of clinical hematoma. Specifically, this research first used feedforward neural network to construct a simulation of edema volume changes, which laid a foundation for subsequent cluster analysis. Then, the k-medoids algorithm was used to divide the different edema patients into three clusters. On this basis, the therapeutic effect of multiple treatment modalities on link edema was analyzed by controlling for a single variable. Finally, Pearson correlation analysis was used to explore the treatment options most related to the volume of hematoma. In general, this research successfully applied the data mining technique and k-medoids clustering algorithm to the field of clinical medicine, which provides a useful reference for the application of more computer analysis methods in the field of medical and health care. In the future, we can further explore how to integrate more machine learning algorithms into medical data analysis, to provide more intelligent support for clinical diagnosis and treatment.