Open AccessAIPSYCH: A Mobile Application-based Artificial Psychiatrist for Predicting Mental Illness and Recovery Suggestions among Students
Author(s) -
Faruk Hossen,
Sajedul Talukder,
Refatul Fahad
Publication year - 2022
Publication title -
international journal of artificial intelligence and applications
Language(s) - English
Resource type - Journals
eISSN - 0976-2191
pISSN - 0975-900X
DOI - 10.5121/ijaia.2022.13204
Subject(s) - mental illness , covid-19 , government (linguistics) , support vector machine , random forest , artificial intelligence , guideline , psychology , computer science , machine learning , mental health , applied psychology , psychiatry , medicine , infectious disease (medical specialty) , disease , linguistics , philosophy , pathology
COVID-19’s outbreak affected and compelled people from all walks of life to self-quarantine in their houses in order to prevent the virus from spreading. As a result of adhering to the exceedingly strict guideline, many people developed mental illnesses. Because the educational institution was closed at the time, students remained at home and practiced self-quarantine. As a result, it is necessary to identify the students who developed mental illnesses at that time. To develop AiPsych, a mobile application-based artificial psychiatrist, we train supervised and deep learning algorithms to predict the mental illness of students during the COVID-19 situation. Our experiment reveals that supervised learning outperforms deep learning, with a 97% accuracy of the Support Vector Machine (SVM) for mental illness prediction. Random Forest (RF) achieves the best accuracy of 91% for the recovery suggestion prediction. Our android application can be used by parents, educational institutes, or the government to get the predicted result of a student’s mental illness status and take proper measures to overcome the situation.