Stress is the most prevailing and global psychological condition that inevitably disrupts the mood and behavior of individuals. Chronic stress may gravely affect the physical, mental, and social behavior of victims and consequently induce myriad critical human disorders. Herein, a review has been presented where supervised learning (SL) and soft computing (SC) techniques used in stress diagnosis have been meticulously investigated to highlight the contributions, strengths, and challenges faced in the implementation of these methods in stress diagnostic models. A three-tier review strategy comprising of manuscript selection, data synthesis, and data analysis was adopted. The issues in SL strategies and the potential possibility of using hybrid techniques in stress diagnosis have been intensively investigated. The strengths and weaknesses of different SL (Bayesian classifier, random forest, support vector machine, and nearest neighbours) and SC (fuzzy logic, nature-inspired, and deep learning) techniques have been presented to obtain clear insights into these optimization strategies. The effects of social, behavioral, and biological stresses have been highlighted. The psychological, biological, and behavioral responses to stress have also been briefly elucidated. The findings of the study confirmed that different types of data/signals (related to skin temperature, electro-dermal activity, blood circulation, heart rate, facial expressions, etc.) have been used in stress diagnosis. Moreover, there is a potential scope for using distinct nature-inspired computing techniques (Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Whale Optimization Algorithm, Butterfly Optimization, Harris Hawks Optimizer, and Crow Search Algorithm) and deep learning techniques (Deep-Belief Network, Convolutional-Neural Network, and Recurrent-Neural Network) on multimodal data compiled using behavioral testing, electroencephalogram signals, finger temperature, respiration rate, pupil diameter, galvanic-skin-response, and blood pressure. Likewise, there is a wider scope to investigate the use of SL and SC techniques in stress diagnosis using distinct dimensions such as sentiment analysis, speech recognition, handwriting recognition, and facial expressions. Finally, a hybrid model based on distinct computational methods influenced by both SL and SC techniques, adaption, parameter tuning, and the use of chaos, levy, and Gaussian distribution may address exploration and exploitation issues. However, factors such as real-time data collection, bias, integrity, multi-dimensional data, and data privacy make it challenging to design precise and innovative stress diagnostic systems based on artificial intelligence.

压力是最普遍的，全球性的心理状况，不可避免地会破坏个人的情绪和行为。慢性压力可能会严重影响受害者的身体，心理和社会行为，并因此引发无数严重的人类疾病。在本文中，已经进行了综述，其中认真研究了用于压力诊断的监督学习（SL）和软计算（SC）技术，以突出强调在压力诊断模型中实施这些方法所面临的贡献，优势和挑战。采用了包括稿件选择，数据综合和数据分析在内的三层审查策略。SL策略中的问题以及在压力诊断中使用混合技术的潜在可能性已得到深入研究。提出了不同的SL（贝叶斯分类器，随机森林，支持向量机和最近的邻居）和SC（模糊逻辑，自然启发和深度学习）技术的优缺点，以获取对这些优化策略的清晰见解。社会，行为和生物压力的影响已得到强调。还简要阐明了对压力的心理，生物学和行为反应。研究结果证实，在压力诊断中使用了不同类型的数据/信号（与皮肤温度，皮肤电活动，血液循环，心率，面部表情等有关）。此外，使用独特的自然启发式计算技术（遗传算法，粒子群优化，蚁群优化，鲸鱼优化算法，蝴蝶优化，Harris Hawks优化器和乌鸦搜索算法）和深度学习技术（深度信念网络，卷积神经网络和递归神经网络），用于使用行为测试，脑电图信号，手指温度编译的多峰数据，呼吸频率，瞳孔直径，皮肤电反应和血压。同样，在情感分析，情感识别，语音识别，手写识别和面部表情等不同维度的压力诊断中，使用SL和SC技术的研究范围也更广。最后，基于受SL和SC技术，适应，参数调整以及使用混沌，征税和高斯分布影响的不同计算方法的混合模型可能会解决勘探和开发问题。