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To enhance public health and reduce medical costs, this paper employs a knowledge map to establish a virtual health community. It integrates valuable medical knowledge using a knowledge fusion algorithm and facilitates online interaction among patients. Utilizing the K-means Clustering Algorithm (K-MEANS) in clustering analysis, users’ needs are consolidated, leading to the creation of a personalized recommendation platform tailored to users’ requirements. The test results show that (1) Angiotensin-Converting Enzyme Inhibitors (ACEI) drugs are effective in treating diabetes and hyperlipidemia, the complications of hypertension, and the trust function values are all above 40%. Calcium Channel Blocker (CCBs) drugs only perform well in the treatment of coronary heart disease, with the trust function value of 47.6%, and the trust function of other hypertension complications does not exceed 30%. Angiotensin Receptor Blocker (ARB) drugs have excellent performance in urine protein, and the trust function value is as high as 52.2%. (2) The accuracy score of the recommended model under K-MEANS decreases with the increase of the number of neighbors and reaches the lowest value when the number of neighbors is 40. The lower the score, the better the accuracy of the model, and the sparsity score of the model data decreases from 10 to 35. However, when the number of neighbors is 35, the score rises, and the overall sparsity score of the model is low, which proves that the model has the ability to deal with data sparsity and makes personalized recommendation of virtual health community more accurate. The objective of this paper is to promote the dissemination of medical knowledge, cater to diverse medical needs, boost online interaction between doctors and patients, enhance the accessibility of healthcare, and contribute to the advancement of public health through personalized recommendations and knowledge integration.
Ma et al. (Thu,) studied this question.