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 child ren often relies on testing, which can lead to delays in treatment. Machine learning (ML) has become a powerful tool for analyzing complex data with the ability to identify subtle patterns associated with mental health. This article explores the potential of machine learning models for early detect ion of mental health problems in children, focusing on accuracy of facts, timeliness of intervention, and ethical considerations r elated to data privacy and algorithmic bias.
Keywords: Mental Health, Machine Learning, Timeliness of Intervention.
Scope of the Article: Public Health