Data Mining and Machine Learning based Student Mental Health Identification across Telangana Regions in India
Year: 2025
Type: book-chapter
Abstract: The proposed work uses a multifaceted approach to address mental health concerns among students, incorporating various machine learning algorithms to improve accuracy and efficacy.Mental health plays a vital role in the overall success of a student, both in school and in life.When students take care of their mental well-being, they tend to excel academically, build stronger relationships, and lead more fulfilling lives.Prioritizing mental health can truly make a difference in how students experience their education and personal growth.Using the power of Logistic Regression, Support Vector Classifier (SVC), Stacking Classifier, and the proposed methodology, we offer a sophisticated platform to assess mental health levels and provide personalized recommendations for treatment and relaxation.The system identifies early signs of mental health problems by analyzing various data sources and user preferences and delivers tailored interventions.Through a combination of advanced analytics and user-centric design, the current work aims to promote proactive mental health management and foster a supportive academic environment conducive to well-being.In addition, the results enhance the importance of machine learning algorithms in the field of health care, especially the consideration of categories such as relationships, health, career, and the results related to it. .
Authors: G. Merlin Linda, Sudheer Reddy Bandi, B. Yugandhara Chary, N. Kamala Vikasini, D. Kavya, D. Kalyani, B. Roshan
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4415763638