To design machine learning classifiers to facilitate the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the differential diagnosis of patients with asthma and restrictive respiratory diseases. FOT and spirometric exams were performed in 97 individuals, including controls (n = 20), asthmatic patients (n = 38), and restrictive (n = 39) patients. The first experiment of this study showed that the best FOT parameter was the resonance frequency, providing moderate accuracy (AUC = 0.87). In the second experiment, a neuro-fuzzy classifier and different supervised machine learning techniques were investigated, including k-nearest neighbors, random forests, AdaBoost with decision trees, and support vector machines with a radial basis kernel. All classifiers achieved high accuracy (AUC ≥ 0.9) in the differentiation between patient groups. In the third and fourth experiments, the use of different feature selection techniques allowed us to achieve high accuracy with only three FOT parameters. In addition, the neuro-fuzzy classifier also provided rules to explain the classification. Neuro-fuzzy and machine learning classifiers can aid in the differential diagnosis of patients with asthma and restrictive respiratory diseases. They can assist clinicians as a support system providing accurate diagnostic options.

设计机器学习分类器，以促进临床应用并提高强迫振荡技术（FOT）在哮喘和限制性呼吸系统疾病患者的鉴别诊断中的准确性。对97名患者进行了FOT和肺活量检查，包括对照组（n  = 20），哮喘患者（n  = 38）和限制性 患者（n = 39）。这项研究的第一个实验表明，最佳的FOT参数是共振频率，可提供中等精度（AUC = 0.87）。在第二个实验中，研究了神经模糊分类器和不同的监督机器学习技术，其中包括k-最近邻居，随机森林，带有决策树的AdaBoost以及带有径向基核的支持向量机。所有分类器在患者组之间的区分中均达到了很高的准确性（AUC≥0.9）。在第三个和第四个实验中，使用不同的特征选择技术使我们仅使用三个FOT参数即可实现高精度。此外，神经模糊分类器还提供了解释分类的规则。神经模糊和机器学习分类器可以帮助对哮喘和限制性呼吸系统疾病的患者进行鉴别诊断。他们可以协助临床医生作为提供准确诊断选项的支持系统。