A Data-Driven Approach to Depression and Quality of Life Assessment Using Machine Learning

EasyChair Preprint 12735

Abstract

research proposal introduces an innovative application that utilizes machine learning to assess an individual's depression level and quality of life in real-time. The application offers personalized insights into the user's mental health and utilizes a distributed ledger system (DLS) that ensures data protection through decentralization and tamper-proof features, safeguarding it from unauthorized access. Additionally, the app utilizes models to provide tailored recommendations and interventions for enhancing mental health and overall well-being. Users will benefit from personalized guidance, including lifestyle adjustments, mindfulness exercises, and access to professional support.

Keyphrases: Depression recommendation, mental health, quality of life

@booklet{EasyChair:12735,
  author    = {Paritosh Sahu and Naman Kumar Sharma and T Balachander},
  title     = {A Data-Driven Approach to Depression and Quality of Life Assessment Using Machine Learning},
  howpublished = {EasyChair Preprint 12735},
  year      = {EasyChair, 2024}}