Predicting Mental Health Illness using Machine Learning Algorithms Article Swipe
K Vaishnavi
,
Ullas Kamath
,
B. Ashwath Rao
,
N. V. Subba Reddy
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2161/1/012021
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2161/1/012021
Early detection of mental health issues allows specialists to treat them more effectively and it improves patient’s quality of life. Mental health is about one’s psychological, emotional, and social well-being. It affects the way how one thinks, feels, and acts. Mental health is very important at every stage of life, from childhood and adolescence through adulthood. This study identified five machine learning techniques and assessed their accuracy in identifying mental health issues using several accuracy criteria. The five machine learning techniques are Logistic Regression, K-NN Classifier, Decision Tree Classifier, Random Forest, and Stacking. We have compared these techniques and implemented them and also obtained the most accurate one in Stacking technique based with an accuracy of prediction 81.75%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2161/1/012021
- https://iopscience.iop.org/article/10.1088/1742-6596/2161/1/012021/pdf
- OA Status
- diamond
- Cited By
- 74
- References
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205970017
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4205970017Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/2161/1/012021Digital Object Identifier
- Title
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Predicting Mental Health Illness using Machine Learning AlgorithmsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
K Vaishnavi, Ullas Kamath, B. Ashwath Rao, N. V. Subba ReddyList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2161/1/012021Publisher landing page
- PDF URL
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https://iopscience.iop.org/article/10.1088/1742-6596/2161/1/012021/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/1742-6596/2161/1/012021/pdfDirect OA link when available
- Concepts
-
Machine learning, Random forest, Mental health, Artificial intelligence, Decision tree, Logistic regression, Classifier (UML), Computer science, Decision tree learning, Mental illness, Psychology, Algorithm, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
74Total citation count in OpenAlex
- Citations by year (recent)
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2025: 18, 2024: 37, 2023: 14, 2022: 5Per-year citation counts (last 5 years)
- References (count)
-
5Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Early | 1 |
| abstract_inverted_index.about | 24 |
| abstract_inverted_index.acts. | 40 |
| abstract_inverted_index.based | 112 |
| abstract_inverted_index.every | 47 |
| abstract_inverted_index.life, | 50 |
| abstract_inverted_index.life. | 20 |
| abstract_inverted_index.stage | 48 |
| abstract_inverted_index.study | 58 |
| abstract_inverted_index.their | 66 |
| abstract_inverted_index.these | 97 |
| abstract_inverted_index.treat | 10 |
| abstract_inverted_index.using | 73 |
| abstract_inverted_index.Mental | 21, 41 |
| abstract_inverted_index.Random | 90 |
| abstract_inverted_index.allows | 7 |
| abstract_inverted_index.feels, | 38 |
| abstract_inverted_index.health | 5, 22, 42, 71 |
| abstract_inverted_index.issues | 6, 72 |
| abstract_inverted_index.mental | 4, 70 |
| abstract_inverted_index.social | 29 |
| abstract_inverted_index.81.75%. | 118 |
| abstract_inverted_index.Forest, | 91 |
| abstract_inverted_index.affects | 32 |
| abstract_inverted_index.machine | 61, 79 |
| abstract_inverted_index.one’s | 25 |
| abstract_inverted_index.quality | 18 |
| abstract_inverted_index.several | 74 |
| abstract_inverted_index.thinks, | 37 |
| abstract_inverted_index.through | 55 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Decision | 87 |
| abstract_inverted_index.Logistic | 83 |
| abstract_inverted_index.Stacking | 110 |
| abstract_inverted_index.accuracy | 67, 75, 115 |
| abstract_inverted_index.accurate | 107 |
| abstract_inverted_index.assessed | 65 |
| abstract_inverted_index.compared | 96 |
| abstract_inverted_index.improves | 16 |
| abstract_inverted_index.learning | 62, 80 |
| abstract_inverted_index.obtained | 104 |
| abstract_inverted_index.Stacking. | 93 |
| abstract_inverted_index.childhood | 52 |
| abstract_inverted_index.criteria. | 76 |
| abstract_inverted_index.detection | 2 |
| abstract_inverted_index.important | 45 |
| abstract_inverted_index.technique | 111 |
| abstract_inverted_index.adulthood. | 56 |
| abstract_inverted_index.emotional, | 27 |
| abstract_inverted_index.identified | 59 |
| abstract_inverted_index.prediction | 117 |
| abstract_inverted_index.techniques | 63, 81, 98 |
| abstract_inverted_index.Classifier, | 86, 89 |
| abstract_inverted_index.Regression, | 84 |
| abstract_inverted_index.adolescence | 54 |
| abstract_inverted_index.effectively | 13 |
| abstract_inverted_index.identifying | 69 |
| abstract_inverted_index.implemented | 100 |
| abstract_inverted_index.patient’s | 17 |
| abstract_inverted_index.specialists | 8 |
| abstract_inverted_index.well-being. | 30 |
| abstract_inverted_index.psychological, | 26 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5080386523, https://openalex.org/A5013789773, https://openalex.org/A5063858151, https://openalex.org/A5044492465 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I164861460 |
| citation_normalized_percentile.value | 0.99576149 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |