Li Mao, Li Fanfan
Sichuan Vocational College of Health and Rehabilitation, Zigong, Sichuan, China.
Student Affairs Department, Huanggang Normal University, Huanggang, Hubei, China.
PeerJ Comput Sci. 2023 Sep 27;9:e1586. doi: 10.7717/peerj-cs.1586. eCollection 2023.
The dynamic landscape of public health occurrences presents a formidable challenge to the emotional well-being of college students, necessitating a precise appraisal of their mental health (MH) status. A pivotal metric in this realm is the Mental Health Assessment Index, a prevalent gauge utilized to ascertain an individual's psychological well-being. However, prevailing indices predominantly stem from a physical vantage point, neglecting the intricate psychological dimensions. In pursuit of a judicious evaluation of college students' mental health within the crucible of public health vicissitudes, we have pioneered an innovative metric, underscored by temporal perception, in concert with a hybrid clustering algorithm. This augmentation stands poised to enrich the extant psychological assessment index framework. Our approach hinges on the transmutation of temporal perception into a quantifiable measure, harmoniously interwoven with established evaluative metrics, thereby forging a novel composite evaluation metric. This composite metric serves as the fulcrum upon which we have conceived a pioneering clustering algorithm, seamlessly fusing the fireworks algorithm with K-means clustering. The strategic integration of the fireworks algorithm addresses a noteworthy vulnerability inherent to K-means-its susceptibility to converging onto local optima. Empirical validation of our paradigm attests to its efficacy. The proposed hybrid clustering algorithm aptly captures the dynamic nuances characterizing college students' mental health trajectories. Across diverse assessment stages, our model consistently attains an accuracy threshold surpassing 90%, thus outshining existing evaluation techniques in both precision and simplicity. In summation, this innovative amalgamation presents a formidable stride toward an augmented understanding of college students' mental well-being during times of fluctuating public health dynamics.
公共卫生事件的动态格局给大学生的情绪健康带来了巨大挑战，因此有必要对他们的心理健康（MH）状况进行精确评估。这一领域的一个关键指标是心理健康评估指数，这是一种常用的衡量标准，用于确定个人的心理健康状况。然而，现有的指数主要源于身体层面的视角，忽略了复杂的心理维度。为了在公共卫生变迁的背景下明智地评估大学生的心理健康，我们开创了一种创新指标，以时间感知为重点，并结合了混合聚类算法。这一改进有望丰富现有的心理评估指数框架。我们的方法依赖于将时间感知转化为可量化的度量，并与既定的评估指标和谐交织，从而形成一种新颖的综合评估指标。这个综合指标是我们构思一种开创性聚类算法的支点，该算法将烟花算法与K均值聚类无缝融合。烟花算法的战略整合解决了K均值固有的一个显著弱点——它容易收敛到局部最优解。对我们的范式进行实证验证证明了其有效性。所提出的混合聚类算法恰当地捕捉了大学生心理健康轨迹的动态细微差别。在不同的评估阶段，我们的模型始终达到超过90%的准确率阈值，从而在精度和简易性方面都优于现有的评估技术。总之，这种创新融合朝着在公共卫生动态变化时期增强对大学生心理健康的理解迈出了重要一步。
