An Enhanced Stress based Hairfall Detection and Prevention using KNN and Machine Learning Techniques

L. Srinivasan, A. Jeevika, R. Navina, S. Priyadharshini
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Abstract

Numerous factors might affect a person's stress level, which results in hair loss. Due to variables such as increased employee dominance, job pressure, and work overload, among others employees in IT sectors are more prone to experience stress. Depression, anxiety, somatization, and attention deficit disorder are just a few of the mental health issues that stress can lead to, and even mortality. As a result, it's critical to recognize human stress early so that the proper treatments may be given and tension can be reduced. Numerous studies have been conducted on stress prediction. An extension of the skin, hair is an essentialcomponent of a person's facial beauty. The outcomes of some learning algorithms, like KNN, are superior. Other intelligent methods such as ML algorithms can be used to diagnose the diseases.
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利用KNN和机器学习技术增强基于应力的毛发脱落检测和预防
许多因素可能会影响一个人的压力水平,从而导致脱发。由于员工主导地位、工作压力和工作过载等变量的增加,IT部门的员工更容易感受到压力。抑郁、焦虑、躯体化和注意力缺陷障碍只是压力可能导致的精神健康问题中的一小部分,甚至会导致死亡。因此,及早认识到人类的压力是至关重要的,这样才能给予适当的治疗,减轻紧张情绪。人们对应力预测进行了大量的研究。头发是皮肤的延伸,是一个人面部美丽的重要组成部分。一些学习算法的结果,比如KNN,是更好的。其他智能方法,如ML算法,可用于诊断疾病。
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