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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63489
Volume:
10 |
5 |
May 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Computer Applications
Bapuji Institute of Engineering and Technology
Master of Computer Applications
Bapuji Institute of Engineering and Technology
The Mental Health Assessment approach will help identify students with mental health issues such as stress, anxiety, and depression early on. Based on their academic standing, emotional scores, lifestyle, and behavioral preferences, kids are divided into groups such as Healthy At-Risk and Struggling using a variety of machine learning models. Furthermore, accuracy can also improve at the diagnosis stage, where the collected data is used to identify the condition of the system. In that case, there is less need. to rely on subjective judgments about the learners’ mental state, which is often inconsistent and, honestly, not very reliable. The system includes. several features used to predict needs and distribute resources meant to support learners’ mental well-being. More importantly, the model gives administrators a practical way. to monitor users, analyse different forms of data, and adjust records related to the relaxation techniques carried out by the children. That part matters a lot, since static records are rarely useful. in real settings. At the same time, the system is designed not only to gather information on counselling and relaxation activities. It also allows. learners to log in, complete diagnosis tasks, and check the state of their own mental well-being. This makes the process more active for learners instead of leaving everything. in the hands of school staff. The present paper mainly focuses on improving learners’ experience through the use of machine learning algorithms. In this context, the idea of wellness. is central, not secondary, in addressing mental wellness problems.
KEYWORDS
The Machine Learning-Based Mental Health Assessment System for Students is indeed a remarkable system that enables the determination of whether students display symptoms of mental illness such as stress, anxiety, and depression. This is because, due to the incorporation of different machine learning algorithms and the evaluation of numerous behavioral, academic, and emotional attributes, classification of students according to their mental health condition has become possible; hence, it enables the grouping of students as either Healthy, At-Risk, or Struggling. In other words, there are no shortcomings associated with the system when contrasted with the manual assessment systems. Finally, with the help of the system, students will be able to access the website and determine their mental health status alongside receiving advice on how to alleviate their mental states through relaxation techniques and seeking counseling services. Furthermore, the system will afford the possibility to administer information related to users, counselors, and mental wellness resources.
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Publication Date
5/20/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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