Assessing Depression Health Information Using Machine Learning

Jebaveerasingh Jebadurai, W. Maria Lebina, V. Shwetha*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Evaluating health information using machine learning is a must, especially with the tremendous growth of internet resources. Increased usage of technology may result in a less working lifestyle. Furthermore, continual stress on an individual might increase the likelihood of psychosis. Peer pressure, heart attacks, despair, and a variety of other effects are examples of these ailments. Health information should be accurate in most cases People browse the internet before seeing a doctor. Our main idea is the process of evaluating depression treatment guidelines without automation High-precision medical professional intervention. In our idea we used Naive Bayes classifier with high text classification accuracy. In front When using a naive Bayes classifier, treatment guidelines are cleaned up by Stop With words derived from NLTK to avoid meaningless words. Words-in-a-Bag By constructing a recurrence matrix, the technique is utilized to calculate the number of words. The final product is available as a web application.

Original languageEnglish
Title of host publicationInternet of Things - 3rd International Conference, ICIoT 2022, Revised Selected Papers
EditorsRevathi Venkataraman, Annie Uthra, R.I. Minu, Vijayan Sugumaran, Pethuru Raj Chelliah
PublisherSpringer Science and Business Media Deutschland GmbH
Pages45-53
Number of pages9
ISBN (Print)9783031284748
DOIs
Publication statusPublished - 2023
Event3rd International Conference on Internet of Things, ICIoT 2022 - Chennai, India
Duration: 05-04-202207-04-2022

Publication series

NameCommunications in Computer and Information Science
Volume1727 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd International Conference on Internet of Things, ICIoT 2022
Country/TerritoryIndia
CityChennai
Period05-04-2207-04-22

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics

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