UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 6
June-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2106702


Registration ID:
311379

Page Number

f23-f27

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Title

Escape The Pain - A Deep Learning Approach for Assisting Depression

Abstract

Depression is a psychological disorder that has influenced various factors, including stress, life-style, physical activity, and physical health. It comes with symptoms such as persistent depression, frustration, and attempts to commit suicide. In health care, it is necessary to accurately predict various life situations. Therefore, the concern for this psychological condition, you should recognize the status of an individual, and guide them. Mental health disorders, as a rule, are accompanied by depression. However, it is not the case that there is a great deal of research, and to predict the situations in order to stop the great depression. Therefore, to find the most accurate model to predict depression, we have been working with a number of models for predicting the risk of major depression. In the field of mental health care and treatment, we use the model to determine the condition of the patient, with the aid of a machine learning algorithm. In this paper, we have analyzed the different machine learning algorithms which can predict depression such as Decision Tree, Extra Trees (Ensemble Technique), Random Forest, Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbour (K-3), Naives Bayes are analyzed to find more accurate model to predict depression. This paper also states that the decision tree has a higher accuracy of 85.75%. The proposed model will take the form of information in order to predict the situations and circumstances that may affect the depression, taking into account the contextual information.

Key Words

Decision Tree Algorithm, Depression risk, Mental health, Machine Learning

Cite This Article

"Escape The Pain - A Deep Learning Approach for Assisting Depression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.f23-f27, June-2021, Available :http://www.jetir.org/papers/JETIR2106702.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Escape The Pain - A Deep Learning Approach for Assisting Depression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppf23-f27, June-2021, Available at : http://www.jetir.org/papers/JETIR2106702.pdf

Publication Details

Published Paper ID: JETIR2106702
Registration ID: 311379
Published In: Volume 8 | Issue 6 | Year June-2021
DOI (Digital Object Identifier):
Page No: f23-f27
Country: Mumbai, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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