Wart is a disease caused by human papillomavirus with common and plantar warts as general forms. Commonly used methods to treat warts are immunotherapy and cryotherapy. The selection of proper treatment is vital to cure warts. This paper establishes a classification and regression tree (CART) model based on particle swarm optimisation to help patients choose between immunotherapy and cryotherapy. The proposed model can accurately predict the response of patients to the two methods. Using an improved particle swarm algorithm (PSO) to optimise the parameters of the model instead of the traditional pruning algorithm, a more concise and more accurate model is obtained. Two experiments are conducted to verify the feasibility of the proposed model. On the hand, five benchmarks are used to verify the performance of the improved PSO algorithm. On the other hand, the experiment on two wart datasets is conducted. Results show that the proposed model is effective. The proposed method classifies better than k-nearest neighbour, C4.5 and logistic regression. It also performs better than the conventional optimisation method for the CART algorithm. Moreover, the decision tree model established in this study is interpretable and understandable. Therefore, the proposed model can help patients and doctors reduce the medical cost and improve the quality of healing operation.

疣是由人乳头瘤病毒引起的疾病，常见形式为plant疣和plant疣。治疗疣的常用方法是免疫疗法和冷冻疗法。选择适当的治疗方法对于治愈疣至关重要。本文建立了基于粒子群算法的分类回归树（CART）模型，以帮助患者在免疫治疗和冷冻治疗之间进行选择。所提出的模型可以准确地预测患者对这两种方法的反应。使用改进的粒子群算法（PSO）代替传统的修剪算法来优化模型参数，可以获得更简洁，更准确的模型。进行了两个实验，以验证所提出模型的可行性。另一方面，使用五个基准来验证改进的PSO算法的性能。另一方面，在两个疣数据集上进行了实验。结果表明，该模型是有效的。所提出的方法比k近邻，C4.5和逻辑回归更好地分类。与传统的CART算法优化方法相比，它的性能也更好。此外，在这项研究中建立的决策树模型是可以解释和理解的。因此，所提出的模型可以帮助患者和医生减少医疗费用，提高康复手术的质量。在这项研究中建立的决策树模型是可以解释和理解的。因此，所提出的模型可以帮助患者和医生减少医疗费用，提高康复手术的质量。在这项研究中建立的决策树模型是可以解释和理解的。因此，所提出的模型可以帮助患者和医生减少医疗费用，提高康复手术的质量。