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

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.