Machine learning approach to predict the depression in job sectors in Bangladesh
Depression is a significant and growing issue that substantially affects an individual's way of life, interrupting typical functioning and blocking viewpoints. At the same time, they may be unaware they are suffering such a problem. This research focuses on depression prediction and determines...
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| Published in: | Current research in behavioral sciences Vol. 2; p. 100058 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01-11-2021
Elsevier |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Depression is a significant and growing issue that substantially affects an individual's way of life, interrupting typical functioning and blocking viewpoints. At the same time, they may be unaware they are suffering such a problem. This research focuses on depression prediction and determines which sex is sadder and more satisfied with their employment. The writers gathered data from both men and females to get accurate statistics. We used factor analysis, Random Forest Classifier, Random Forest Regression, Naive Bayes, and K Neighbors Classifier algorithms to determine which sources of stress predict stress-related symptoms in people exploring job satisfaction as predicted and job depression by age, monthly income, gender, occupation, children, city, previous job, marital status, and current job satisfaction level.
•Prediction of depression based on satisfaction in jobs bases on sex and other features.•Data analysis and data visualization and different ML algorithms have been applied to the collected dataset to predict depression.•The prediction was done through the collected dataset from both males and females.•Satisfaction level was one of the main features to detect depression in jobs.•Finding the accuracy of the algorithms that predicted the depression in jobs.
Depression is an important and growing problem that affects the way a person lives, disturbs the functioning properties, and impedes views while being unconscious of the way they experience such an illness. This study focuses on predicting depression and showing which sex is most unhappy and happy with their work. To get reliable results, the authors gathered data from both males and women. The data used by the auto-report questionnaires were used to determine the sources of stress that predict stress symptoms among people who are exploring job satisfaction as expected and work depressed by the following: age, monthly income, gender, occupation, children, city, job before, marital status, satisfaction level of the current job, face harassment to the job, satisfaction level, opinion of a colleague and relative. Random Forest Regressor, Random Forestry Classifying Algorithms were used. [Display omitted] |
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| ISSN: | 2666-5182 2666-5182 |
| DOI: | 10.1016/j.crbeha.2021.100058 |