The present study is aimed at identifying the most prominent determinants of OCD along with their strength to classify the OCD patients from healthy controls. The data for this cross-sectional study were collected from 200 diagnosed OCD patients and 400 healthy controls. The respondents were selected through purposive sampling and interviewed by using the Y-BOCS scale with the addition of a factor, worth of an individual in his family. The validity and reliability of data were assessed through Cronbach’s alpha and confirmatory factor analysis. Artificial Neural Network (ANN) modeling was adopted to determine threatening determinants along with their strength to predict OCD in an individual. The results of ANN modeling depicted 98% accurate classification of OCD patients from healthy controls. The most contributing factors in determining the OCD patients according to normalized importance were the contamination and cleaning (100%); symmetric and perfection (72.5%); worth of an individual in the family (71.1%); aggressive, religious, and sexual obsession (50.5%); high-risk assessment (46.0%); and somatic obsessions and checking (24.0%).

中文翻译：

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识别强迫症的症状严重程度以进行分类和预测：人工神经网络方法。


本研究旨在确定强迫症最重要的决定因素及其将强迫症患者与健康对照进行分类的强度。这项横断面研究的数据收集自 200 名确诊的强迫症患者和 400 名健康对照者。受访者是通过有目的的抽样选出的，并使用 Y-BOCS 量表进行访谈，并添加了一个因素，即家庭中个人的价值。通过Cronbach's alpha 和验证性因子分析评估数据的有效性和可靠性。采用人工神经网络（ANN）模型来确定威胁性决定因素及其预测个体强迫症的强度。 ANN 建模的结果显示，强迫症患者与健康对照者的分类准确率高达 98%。根据标准化重要性确定强迫症患者的最重要因素是污染和清洁（100%）；对称、完美（72.5%）；家庭中个人的价值（71.1%）；攻击性、宗教和性痴迷（50.5%）；高风险评估（46.0%）；以及身体的痴迷和检查（24.0%）。