Psychological disorders, notably social anxiety and depression, exert detrimental effects on university students, impeding academic achievement and overall development. Timely identification of interpersonal sensitivity becomes imperative to implement targeted support and interventions. This study selected 958 freshmen from higher education institutions in Zhejiang province as the research sample. Utilizing the runge-kutta search and elite levy spreading enhanced moth-flame optimization (MFO) in conjunction with the kernel extreme learning machine (KELM), we propose an efficient intelligent prediction model, namely bREMFO-KELM, for predicting the interpersonal sensitivity of college students. IEEE CEC 2017 benchmark functions and the interpersonal sensitivity dataset were employed as the basis for detailed comparisons with peer-reviewed studies and well-known machine learning models. The experimental results demonstrate the outstanding performance of the bREMFO-KELM model in predicting the sensitivity of interpersonal relationships in college students, achieving an impressive accuracy rate of 97.186%. In-depth analysis reveals that the prediction of interpersonal sensitivity in college students is closely associated with multiple features, including easily hurt in relationships, shy and uneasy with the opposite sex, feeling inferior to others, discomfort when observed or discussed, and blame and criticize others. These features are not only crucial for the accuracy of the prediction model but also provide valuable information for a deeper understanding of the sensitivity of college students' interpersonal relationships. In conclusion, the bREMFO-KELM model excels not only in performance but also possesses a high degree of interpretability, providing robust support for predicting the sensitivity of interpersonal relationships in college students.

中文翻译：

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预测人际敏感性：大学生心理障碍早期干预的预测模型

心理障碍，特别是社交焦虑和抑郁，对大学生产生有害影响，阻碍学业成绩和全面发展。及时识别人际敏感性对于实施有针对性的支持和干预至关重要。本研究选取了浙江省高等院校958名新生作为研究样本。利用龙格库塔搜索和精英征传播增强飞蛾火焰优化（MFO）结合内核极限学习机（KELM），我们提出了一种高效的智能预测模型，即bREMFO-KELM，用于预测大学的人际敏感性学生。采用 IEEE CEC 2017 基准函数和人际敏感性数据集作为与同行评审研究和知名机器学习模型进行详细比较的基础。实验结果表明，bREMFO-KELM模型在预测大学生人际关系敏感性方面表现出色，准确率高达97.186%。深入分析发现，大学生人际敏感性的预测与多种特征密切相关，包括在人际关系中容易受到伤害、对异性感到害羞和不安、自卑、被观察或讨论时感到不适、责备和批评等。其他的。这些特征不仅对于预测模型的准确性至关重要，而且为更深入地了解大学生人际关系的敏感性提供了有价值的信息。综上所述，bREMFO-KELM模型不仅性能优异，而且具有高度的可解释性，为预测大学生人际关系敏感性提供了有力的支持。