Mental Health Prediction Using Data Analysis

EasyChair Preprint 10053

Abstract

Mental and behavioral problems exist in all societies, in all phases of life, in men and women, in the rich and poor, and in rural and urban populations. Approximately 450 million individuals globally are believed to be experiencing a mental or neurological disorder., including behavioral or substance-related problems, at any given moment. Social networking sites are a common way for people to express their feelings in the modern world. These kinds of emotions are frequently examined to forecast user behavior. This study uses an ensembled deep learning network to characterize these feelings to forecast the user's mental disorder. The analysis is carried out on the Internet social networking platform, and both convolutional and recurrent neural networks are used to create the ensembling deep-learning model. In this study, multiclass classification is used to distinguish between dementia, psychosis, and Alzheimer's disease. The multiclass classification procedure was carried out using the suggested prediction method

Keyphrases: Parkinson, data analysis, mental health, stress

@booklet{EasyChair:10053,
  author    = {Kush Kaushik and Rahul Kapri},
  title     = {Mental Health Prediction Using Data Analysis},
  howpublished = {EasyChair Preprint 10053},
  year      = {EasyChair, 2023}}