过度使用手机的抑郁程度预测:决策树与线性回归算法

Imrus Salehin, Iftakhar Mohammad Talha, Nazmun Nessa Moon, M. Saifuzzaman, Fernaz Narin Nur, Mariom Akter
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引用次数: 8

摘要

在本研究中,我们运用先进的机器学习和回归分析来找出抑郁程度。极端手机的使用会影响人类的行为。抑郁预测是我们研究的主要因素。抑郁的大脑会释放皮质醇、细胞因子和血清素;这三个组成部分对大脑维持情绪和行为是最重要的。我们使用机器学习和数据挖掘算法来测量抑郁程度。我们在医学科学和信息技术的研究领域完成了全部工作,并建立了合作关系。在本研究中,我们重点研究了算法的强度,并用python编程计算了算法的精度。研究结果表明,如果智能移动设备每天使用10到13个小时左右,就会每天改变人类的大脑。最后,我们观察到,一个男人或女人正在慢慢经历一个抑郁症的影响,过度移动操作。在我们的研究中,强度计算方法是一种利用概率、线性回归、决策树和朴素贝叶斯来确定抑郁程度的新方法。为了我们工作的准确性,我们使用了三种类型的算法来寻找最佳的比例和百分比
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the Depression Level of Excessive Use of Mobile Phone: Decision Tree and Linear Regression Algorithm
In this study, we apply advanced machine learning and regression analysis to find out the depression level. The use of extreme mobile phones will impact human behavior. Depression prediction is the main factor in our research. The depressive brain emits cortisol, cytokines, and serotonin; these three components are most important for the brain to maintain emotion and behavior. We measure the depression level using machine learning and data mining algorithms. We have done the whole work in the research area of medical science and information technology and also built up a collaboration. In this study, we focus on the strength of the algorithm and calculate the accuracy with python programming. The result expresses that smart mobile devices change the human brain day by day if they spend more time around 10 to 13 hours a day. Finally, we observed that a man or woman is slowly going through a depression for the impact of the excessive mobile operates. In our study, a strength calculation method is a novel approach to finding out depression level using Probability, Linear regression, Decision tree, and Naive Bayes. For the accuracy of our work, we have used three types of algorithms to find the optimal ratio and percentage
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