摘要：Aiming at the current low pre-diabetes detection rate, this paper proposes a PSO-SVM model to assist doctors in identifying the risk of patients with pre-diabetes. The paper uses the Support Vector Machine as the verification algorithm, takes the radial basis kernel as the kernel function, uses the adaptive Particle Swarm Optimization algorithm to optimize the penalty factor and kernel parameters of the Support Vector Machine, and establishes a PSO-SVM model, finally compares the model with Neural Network, Logistic Regression, and Naive Bayes model, and use Sensitivity, Specificity indicators and ROC curve to evaluate model performance. Empirical analysis proves that the combined model proposed in this paper can effectively identify the risk of patients with prediabetes.
