Classification symptoms of mental health problems among students by using fuzzy inference system

The prevalence of mental health disorders among university students is a growing global concern, significantly impacting their well-being and academic performance. Many students struggle to recognize their mental health conditions and current assessment methods such as the Depression, Anxiety and Stress Scale-12 (DASS-21) utilized by the counselling unit at UiTM Seremban 3, often lack precision. This study addresses this limitation by aiming to identify the key symptoms affecting student’s mental health and developing a more accurate classification model using the Fuzzy Inference System (FIS). The FIS model was constructed through a fivestep process which are defining input and output variables, establishing membership functions, formulating a rule-base, conducting fuzzification and performing defuzzification. The results show that the developed FIS model is able to provide more precise value than the DASS-12, with a percentage error of less than 7% for all symptoms analysed. Furthermore, a comparative analysis of students from the three primary faculties at UiTM Seremban 3 reveals that students from Faculty of Sport Science and Recreation (FSR) experience the highest-level health issues. This finding suggests that targeted mental health interventions and support programs are especially needed for students in the FSR faculty.