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Mental illness has been ranked as one of the diseases with the highest number of cases globally. Generally, an individual can only be diagnosed with mental illness by undergoing proper medical procedures by the experts. On the other hand, due to the presence of different beliefs in various cultures and ethnicities, an individual is also claimed to have mental illness by the assumptions made based on the symptoms that the individual is having. This is additionally contributed by feelings of reluctance and being reserved, where the individual would subsequently resort not to go for a proper medical assessment. Therefore, to overcome this problem, this present project was developed as a real-time platform for users with mental health conditions to freely share their problems. Specifically, a rule-based algorithm of Depression Anxiety Stress Scales (DASS) was implemented in the Emotional Evaluation and Psychological Support (PEEPS). Furthermore, a real-time platform for PEEPS was developed and the effectiveness of the whole system was evaluated by using the Software Usability Measurement Inventory (SUMI). The development of the project would benefit the users to evaluate their own level of depression, stress and anxiety through the DASS test and get necessary counselling through the chat feature. According to the results collected, it was shown that the users were satisfied with the PEEPS application. This application is certainly relevant for governmental and non-governmental institutions that are interested in supporting and improving mental health-related issues. Future studies may consider modifying the machine learning technique to anticipate mental health issues as well as adding a few other elements to the application, such as personalized feedback or patient recommendations.
Rashid et al. (Fri,) studied this question.