Title: A user friendly anger and anxiety disorder prediction scheme using machine learning and a mobile application for mental healthcare
Authors: Mohammad Salah Uddin; Mahfuzulhoq Chowdhury
Addresses: Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong – 4349, Bangladesh ' Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong – 4349, Bangladesh
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.
DOI: 10.1504/IJDATS.2024.137466
International Journal of Data Analysis Techniques and Strategies, 2024 Vol.16 No.1, pp.47 - 81
Received: 02 Aug 2023
Accepted: 04 Dec 2023
Published online: 19 Mar 2024 *