关键词:

就医选择, 智能推荐, 麻雀搜索算法, 支持向量回归

Abstract:

Information explosion makes it difficult for patients to choose a doctor, and it has also limited the efficient use of Internet medical services. In this paper, a study is conducted on an intelligent medical recommendation model that considers the diversity of patient preferences for medical treatment, mainly focusing on the decision-making preferences of patients with sustained usage behavior, such as preferences for doctor service quality, knowledge contribution, electronic reputation, and medical experience, taking into account graded diagnostic and treatment standards when constructing feature variables. Based on the relative quantity, the comprehensive score is calculated by using the entropy weight-TOPSIS multi-index evaluation model, and the score result is used as the target variable, while the support vector regression algorithm optimized by the improved sparrow search algorithm is used to mine the relationship between the feature variable and the target variable, thereby constructing an intelligent medical recommendation model. According to the recommendation model, the comprehensive performance of doctors recommended is better, and the adoption rate of recommendations has also increased.

Key words:

medical selection, intelligent recommendation, sparrow search algorithm, support vector regression