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Stream/Area:
-
Subject:
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Paper ID:
IJSREM64382
Volume:
10 |
5 |
May 2026
Pages:
1-10
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.
Department of Computer Science
Prajyoti Niketan College, Pudukad
Mental health is a complex disease involving numerous individual, socioeconomic, and clinical variables. Mental illness is a health problem that undoubtedly impacts emotions, reasoning, and social interaction of a person. For children, their ability to learn could be interfered by mental disorders. Early detection of mental health issues is an essential procedure. Traditionally, mental health professionals use face-to-face interviews, self-reporting and questionnaire distribution to distinguish between the mental health status of individuals. Data preprocessing is the first step to be performed after collecting the data. It is used to clean and normalise the data before fitting it into a model. It is necessary because unprocessed raw data may contain noise, missing values, and duplicates that could affect the model's accuracy. The next step is to collect the features and select the most discriminate set to identify the disease. For the purpose of mental health prediction, approaches based on machine learning, deep learning and large language models are explored. Commonly used datasets in this domain are also discussed.
KEYWORDS
Mental health is widely discussed topic in every community. Early detection of mental illness is very essential as it will severely affect the individual, family and society. Apart from the traditional method of collecting information through questionnaire, symptoms can be detected through the online text posted by the patient. In this instinct, advances in AI can be utilized for mental health prediction. The different techniques in AI are best utilized for this purpose. The large language models are now capable of analyzing the contents of social media post. The recommendation model can make use of this information and predict the current mental status of the individual. In the future, it is planned to do mental health prediction using multimodal data.
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Publication Date
5/31/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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