Predicting mental depression

K. Jamberi; S. B. Mohan; S. Bhuvana; R. Ashwini; M. Premkumar; A. Rajasekar

Author(s)

K. Jamberi Department of Computer Science, S.A. College of Arts & Science,Chennai,India

S. B. Mohan Departmnet of Electronics and Communication Engineering, S.A. Engineering College,Chennai,India

S. Bhuvana Deparment of Computer Science and Engineering, Dr. MGR Educational and Research Institute,Chennai,India

R. Ashwini Department of Electronics and Communication Engineering, S.A. Engineering College,Chennai,India

M. Premkumar Department of Electronics and Communication Engineering, Panimalar Engineering College,Chennai,India

A. Rajasekar Department of Artificial Intelligence and Data Science, Sri Sai Ram Institute of Technology,Chennai,India

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%.

Publication Details

Page(s) 512-519

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 19, Issue: 4, Year: 2024

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Keywords

neural network tuned multiple linear regression, neural network tuned association based multiple linear regression, depression dataset

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