DEPRESSION ANALYSIS USING DEEP LEARNING MODELS
Author(s):
Bilal Ahmed Reshi, Ankur Gupta
Keywords:
Depression, Machine Learning, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, Random Forest, K Nearest Neighbour Classifier.
Abstract
Depression is one of the severe and grave health disorders that affects the steadiness of mind. It has become a serious issue in the present generation. The total number of cases has been increasing day-by-day due to a number of reasons like stress at school, college, work, personal life, other diseases, etc. Although it has become one of the most common disease, people are still reluctant to talk about it openly due to the fear that others might consider them lunatic. The introduction of Machine Learning into the field of Medicine and Health industry has provided diagnostic tools that are able to enhance the precision and accuracy while reducing the difficult tasks which require the intervention of humans. There is promising evidence that Machine Learning has the capability not only to detect but also significantly enhance the treatment of compound mental conditions such as depression by developing a framework. In the past, Machine Learning Algorithms have been proved to be fairly supportive where researchers worked on the data from social media to foresee the number of persons suffering from this ailment on the basis of their initial symptoms. The main aim is to help those patients who suffer from depression in the early recognition of symptoms of this disease which can prove to be valuable not only to them but also to their families.
Article Details
Unique Paper ID: 156545

Publication Volume & Issue: Volume 9, Issue 3

Page(s): 83 - 88
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