Early Detection and Intervention for Children’s Mental Health Issues Using Machine Learning
Mohamed Safdar B1, Pandiarajan S2
1Mr. Mohamed Safdar B, Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.
2Mr. Pandiarajan S, Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.
Manuscript received on 20 November 2024 | First Revised Manuscript received on 27 November 2024 | Second Revised Manuscript received on 12 December 2024 | Manuscript Accepted on 15 January 2025 | Manuscript published on 30 January 2025 | PP: 14-16 | Volume-5 Issue-2, January 2025 | Retrieval Number: 100.1/ijpmh.B104905020125 | DOI: 10.54105/ijpmh.B1049.05020125
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The rise of mental health problems in children has created a need for early detection and intervention strategies. The routine method of diagnosing mental illness in children often relies on testing, which can lead to delays in treatment. Machine learning (ML) has become a powerful tool for analysing complex data, enabling the identification of subtle patterns associated with mental health. This article examines the potential of machine learning models for the early detection of mental health issues in children, with a focus on the accuracy of findings, timeliness of intervention, and ethical considerations related to data privacy and algorithmic bias.
Keywords: Mental Health, Machine Learning, Timeliness of Intervention.
Scope of the Article: Public Health