Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{179858,
        author = {Prabhat Uikey},
        title = {Design a classifier of medical data using machine learning and GAD-7},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8970-8975},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179858},
        abstract = {Mental health concerns, particularly anxiety disorders, are growing at an alarming rate, making early detection and intervention increasingly important. This project focuses on developing a machine learning-based classifier using the Generalized Anxiety Disorder 7-item (GAD-7) questionnaire to identify individuals who may be experiencing anxiety. The GAD-7 is a validated tool used for measuring the severity of anxiety symptoms, and it forms the core of the dataset used in this study.The proposed system involves preprocessing medical and survey data, followed by training various supervised machine learning models such as logistic regression, support vector machines, decision trees, and random forests. These models are evaluated using key performance metrics including accuracy, precision, recall, and F1-score to determine their effectiveness in classifying anxiety levels. The outcomes of this project suggest that machine learning can play a vital role in mental health assessment by providing quick, reliable classifications. The model can potentially be used in clinical settings to support healthcare providers in diagnosing and monitoring anxiety, thus enabling timely and personalized treatment.},
        keywords = {},
        month = {May},
        }