Department of Health Information Management, School of Management and Medical Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
Department of Health Information Management, School of Management and Medical Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
Comput Methods Programs Biomed. 2020 Jul;190:105354. doi: 10.1016/j.cmpb.2020.105354. Epub 2020 Jan 24.
Mental disorders, according to the definition of World Health Organization, consist of a wide range of signs, which are generally specified by a combination of unusual thoughts, feelings, behavior, and relationships with others. Social anxiety disorder (SAD) is one of the most prevalent mental disorders, described as permanent and severe fear or feeling of embarrassment in social situations. Considering the imprecise nature of SAD symptoms, the main objective of this study was to generate an intelligent decision support system for SAD diagnosis, using Adaptive neuro-fuzzy inference system (ANFIS) technique and to conduct an evaluation method, using sensitivity, specificity and accuracy metrics.
In this study, a real-world dataset with the sample size of 214 was selected and used to generate the model. The method comprised a multi-stage procedure named preprocessing, classification, and evaluation. The preprocessing stage, itself, consists of three steps called normalization, feature selection, and anomaly detection, using the Self-Organizing Map (SOM) clustering method. The ANFIS technique with 5-fold cross-validation was used for the classification of social anxiety disorder.
The preprocessed dataset with seven input features were used to train the ANFIS model. The hybrid optimization learning algorithm and 41 epochs were used as optimal learning parameters. The accuracy, sensitivity, and specificity metrics were reported 98.67%, 97.14%, and 100%, respectively. The results revealed that the proposed model was quite appropriate for SAD diagnosis and in line with findings of other studies. Further research study addressing the design of a decision support system for diagnosing the severity of SAD is recommended.
根据世界卫生组织的定义，精神障碍包括广泛的症状，这些症状通常由异常的思维、感觉、行为和与他人的关系组合而成。社交焦虑障碍（SAD）是最常见的精神障碍之一，其特征是在社交场合中持续存在严重的恐惧或尴尬感。鉴于 SAD 症状的不精确性，本研究的主要目的是使用自适应神经模糊推理系统（ANFIS）技术生成一个用于 SAD 诊断的智能决策支持系统，并使用敏感性、特异性和准确性指标进行评估方法。
本研究选择了一个具有 214 个样本量的真实世界数据集来生成模型。该方法包括预处理、分类和评估三个阶段。预处理阶段本身由三个步骤组成，称为归一化、特征选择和异常检测，使用自组织映射（SOM）聚类方法。使用 5 折交叉验证的 ANFIS 技术用于社交焦虑障碍的分类。
使用具有七个输入特征的预处理数据集来训练 ANFIS 模型。混合优化学习算法和 41 个 epoch 被用作最佳学习参数。报告的准确性、敏感性和特异性度量值分别为 98.67%、97.14%和 100%。结果表明，所提出的模型非常适合 SAD 诊断，与其他研究的结果一致。建议进一步研究设计用于诊断 SAD 严重程度的决策支持系统。
