用于心理健康的机器学习追踪器

Dr. P. Senthilkumar, A. V, Dr.G. Ramesh
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引用次数: 0

摘要

COVID-19对社会最有害的后果之一是它如何影响全球心理健康,产生新的问题并加剧现有问题。当为数不多的资源用于大流行时,心理健康问题和治疗通常会退居次要地位。因此,在任何心理问题失控之前监测它们是很重要的。本研究的目标是开发一种基于机器学习的心理健康追踪器,主要关注认知精神障碍。MMSE测试提供了一种可量化的认知障碍测量方法,并提供了一种追踪认知变化的方法,是用于确保及早发现问题的筛查方法之一。使用K-means聚类算法创建具有潜在痴呆发病率的聚类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MACHINE LEARNING TRACKER FOR MENTAL HEALTH APPROACH
One of the most pernicious consequences of COVID-19 on society is how it has impacted worldwide mental health, creating new problems and aggravating existing ones. When the few resources are geared for the pandemic, mental health issues and therapy typically take a backseat. Therefore, it's important to monitor any psychological issues before they spiral out of control. The goal of this study is to develop a machine learning-based mental health tracker that primarily focuses on cognitive mental disorders. The MMSE test, which offers a quantifiable measure of cognitive impairment and a way to track cognitive changes over time, is one of the screening methods used to make sure that issues are discovered early. Clusters are created using the K-means clustering algorithm with the potential for dementia incidence.
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