|
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter
Copyright Infringement Claims
Indexing Partner
|
Published Paper Details
|
|
| Paper Title: | Healthcare Recommendation System For Depression Using Machine Learning Algorithms |
| Authors Name: | Jerusha S , Deepika R , Hemalatha G , Keerthiga D |
| Unique Id: | IJSDR2401086 |
| Published In: | Volume 9 Issue 1, January-2024 |
| Abstract: | Over the years, stress and anxiety causes major effects in people’s minds worldwide. New technological advancements are changing the future of the healthcare system. Lifestyle is something which defines an individual the best. Lifestyle including factors like income, age group, marital status, child, alcohol consumption and many more affect the quality of life of an individual. Identification of factors that are responsible for causing depression may lead to new experiments and treatments. Because depression as a disease is becoming a leading community health concern worldwide. NHANES is a program of studies designed to assess the health and nutritional status of adults and children. NHANES conducted a survey for analyzing depression and more than 1200 responses were recorded. These responses are used for training the model and for predicting the depression level. Using machine learning techniques this project presents a complete methodological framework to process and explore the heterogeneous data and to better understand the association between factors related to quality of life and depression. With the identified features, different models were chosen and trained accordingly. Random Forest, Logistic Regression and KNN have been chosen. By evaluating the results of various ML algorithms, Random Forest Classifier outperformed all other algorithms in predicting the levels of depression. The RF based prediction model is more accurate and informative in predicting. The final outcome received was 81.22%. Then, according to the level of depression, a recommendation will be given for future therapy. The recommendation will also consider the parameters responsible for the depression. This will help in assistance to other researchers and clinicians with the recognition of risk related to depression and other psychological disorders. |
| Keywords: | depression, random forest, knn, logistic regression, quality of life |
| Cite Article: | "Healthcare Recommendation System For Depression Using Machine Learning Algorithms", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 1, page no.597 - 604, January-2024, Available :http://www.ijsdr.org/papers/IJSDR2401086.pdf |
| Downloads: | 000338720 |
| Publication Details: | Published Paper ID: IJSDR2401086 Registration ID:209931 Published In: Volume 9 Issue 1, January-2024 DOI (Digital Object Identifier): Page No: 597 - 604 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijsdr.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
Track Paper
Important Links
Conference Proposal
ISSN
 |
 |
DOI (A digital object identifier)
  Providing A digital object identifier by DOI How to GET DOI and Hard Copy Related |
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
 |
 |
 |
Social Media
Indexing Partner |
|||
| Copyright © 2024 - All Rights Reserved - IJSDR | |||
Facebook Twitter Instagram LinkedIn