A user friendly anger and anxiety disorder prediction scheme using machine learning and a mobile application for mental healthcare

Mohammad Salah Uddin and Mahfuzulhoq Chowdhury

International Journal of Data Analysis Techniques and Strategies, 2024, vol. 16, issue 1, 47-81

Abstract: The growing prevalence of mental health disorders concerns has motivated the development of innovative technologies to support mental well-being. The previous literary works on mental healthcare did not investigate anger and anxiety disorder prediction by considering 23 features. There is a lack of mental healthcare assistance mobile applications in the literary works by considering anger and anxiety assessment and necessary emergency assistance features. To solve these issues, this paper initiates a machine learning model-based anger and anxiety prediction scheme by examining different machine learning algorithms. Our analytical results show that the logistic regression model shows better prediction results among all machine learning algorithms in terms of higher accuracy, precision, recall, and error rate. This paper presents a mental healthcare mobile application with anger and anxiety assessment, physical exercise suggestions, hospital search, doctor appointment booking, and emergency contact. The evaluation result shows the efficiency of the proposed scheme.

Keywords: anger and anxiety prediction; self-assessment tools; mental healthcare; mobile application; machine learning; logistic regression; support vector machine; SVM; K-nearest neighbours; KNN; decision tree; multi-class classification. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=137466 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:16:y:2024:i:1:p:47-81

Access Statistics for this article

More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().