Predicting the Depression Level of Excessive Use of Mobile Phone: Decision Tree and Linear Regression Algorithm

Abstract

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