Design And Construction Of An Expert System For Early Detection of Mental Illness in COVID-19 Patients And Handling Using Certainty Factor Method

Abstract

Covid-19 is a new type of virus that can be transmitted to humans. This virus spreads very quickly and has spread to almost all countries. In the midst of Covid-19 outbreak, social phenomenon emerged that has the potential to exacerbate the situation, it is negative social stigma against a person or group of people who experience symptoms or have COVID-19 disease. They are labeled, stereotyped, discriminated against, and treated differently because they are associated with Covid-19 disease. This pandemic puts pressure on the emergence of mental illnesses such as fatigue, stress, fear, sadness, loneliness, schizophrenia, anxiety, depression, or post-traumatic stress disorder (PTSD) and its probability of dying from Covid 19 is almost 3 times higher than those who do not have mental illness. Therefore, an expert system is needed to detect mental disorders in Covid-19 patients early as prevention. This system works as psychologist experts detect Covid-19 patients through the people closest to the patient. By observing the conditions and symptoms arose in the patient's psychological condition, the user will fill the data in the system to find out the level of disturbance experienced by the patient in real time. Then, the system will provide solutions, diagnosis results and appropriate treatment methods for patients so that symptoms of mental disorders can be detected and prevented early on without direct contact between patients and experts