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Mental Health Detection: Detection And Classifying Of Anxiety Using Machine Learning

Year: 2025

Type: book-chapter

Abstract: Mental health has recently become a crucial area of concern across various fields, gaining significant attention due to the rising number of individuals suffering from mental disorders, especially anxiety.A large portion of these individuals includes university students, who face immense academic and social pressure.This research aims to assess the prevalence of anxiety among Indian university students, identify its causes and effects, and classify anxiety levels using machine learning techniques.The dataset was collected through a structured questionnaire based on a Likert scale and distributed among 127 engineering students.Statistical analyses, including Cronbach's alpha (0.723) and Pearson's correlation (0.823), were conducted to verify reliability and validity.Machine learning algorithms, including Naïve Bayes, Decision Tree, Random Forest, and SVM, were applied to classify anxiety levels.The accuracy achieved by these models was 71.05%, 71.05%, 78.9%, and 75.5%, respectively, with Random Forest yielding the highest performance.

Source: Advances in computer science research

DOI: 10.2991/978-94-6463-858-5_83

Authors: D. Tejaswi, M. Harshini, G. Shishira, K. Manudeep, K. Rahul

Institution: Anil Neerukonda Institute of Technology & Sciences

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4415763572