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Machine Learning based Detection of Depression and Anxiety
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 45 |
| Year of Publication: 2021 |
| Authors: Guna Sekhar Sajja |
10.5120/ijca2021921856
|
Guna Sekhar Sajja . Machine Learning based Detection of Depression and Anxiety. International Journal of Computer Applications. 183, 45 ( Dec 2021), 20-23. DOI=10.5120/ijca2021921856
Abstract
Anxiety is something that everyone encounters at some point in their lives. Anxiety is a word that is used in everyday situations to represent the discomforts and negative feelings that a person has when they are tense or worried. Machine learning approaches enable computers to create data that may be utilized for factual study to attain a certain range of performances. The use of computer frameworks to automate decision-making based on test data is encouraged throughout the development of the models for the test data. This article presents a model for predicting feelings of anxiety and depression. There is a set of speech data that is used as input into this framework. A preprocessing step was performed on this data set to remove noise from the data and make the original data set more consistent. The input data set is then submitted to a variety of machine learning approaches, including Nave Bayes, Random Forest, and Support Vector Machines (SVM). It is necessary to classify the information. In this section, the categorization results of several approaches are discussed and contrasted.
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