Improved Dirichlet mixture model clustering algorithm for medical data anomaly detection

In order to address the issue of identifying over-diagnosis and anomaly expenses in the healthcare service process, a local outlier mining clustering algorithm (ILOF-DPMM) is proposed by combining the clustering-based local outlier factor (CBLOF) algorithm with Dirichlet mixture model (DPMM). By extracting the patient's hospitalisation records from the medical record homepage, the influencing factors of hospitalisation costs for different disease types are classified, and the random forest method is used to reduce the feature dimension by disease type. The feature extraction and dimensionality reduction methods adopted by this algorithm effectively cluster medical insurance expense data. When calculating the LOF value of data, using a weighted calculation method based on the similarity of discrete and continuous features can more accurately detect abnormal data points in the data set, and has the ability to detect new data in real time, thus improving detection accuracy and efficiency.