Predicting mental depression

Abstract

In this study, a multilevel linear regression technique based on neural network tailored association is suggested to predict human mental depression. The suggested technique uses a neural network configured for association-based multiple linear regression to forecast the mental depression dataset. The spectrum of depression is predicted using a variety of statistical techniques, including both multiple linear regression and linear regression with neural network tuning. When predicting the severity of depression, tweaked algorithms perform less well. They have been fine-tuned for significant differences in the accuracy, timing, and speed of depression predictions. To address these difficulties, a multiple linear regression solution based on neural network tailored association is suggested. The Multiple linear regression using a neural network that has been tweaked for association yields high compared to other statistical approaches, accuracy prediction is roughly 91%.