Mental Health Classification Using Machine Learning with PCA and Logistics Regression Approaches for Decision Making
Mental health statistics come with numerous challenges, beginning with data integrity. Ensuring data accuracy and reliability is essential, especially if these datasets are to be used for advanced analysis or research. Additionally, privacy concerns heavily impact the management of mental health dat...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/84/1/47 |
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| Summary: | Mental health statistics come with numerous challenges, beginning with data integrity. Ensuring data accuracy and reliability is essential, especially if these datasets are to be used for advanced analysis or research. Additionally, privacy concerns heavily impact the management of mental health data. Protecting the privacy and confidentiality of individuals—especially those with personal or sensitive information—is paramount in system development. Robust protocols should be implemented to prevent unauthorized access and potential breaches. Another critical issue is bias in the training data, which can arise from the underrepresentation of certain demographic groups or the overrepresentation of others. Reducing bias within these datasets is essential to enhance the fairness and accuracy of the models and algorithms they support. Research on mental health classification using machine learning techniques, particularly PCA and logistic regression, is significant because it has the potential to improve decision-making in mental health care. |
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| ISSN: | 2673-4591 |