Proposing Two Hybrid Data Mining Models for Discovering Students' Mental Health Problems

Mental health is an important issue for university students. The objective of this article was to apply and compare the different classification methods students’ mental problems. Furthermore, it presents ensemble method improve accuracy classifiers assist psychologists in decision making process. For this, 10 were applied classifying students into two groups. In addition, combining are presented. first proposed method, selected based on their accuracy, then voting carried out maximum probability. second combined fields confusion table, majority scheme. These evaluated ways. Focusing probability voting, 92.24%, whereas 95.97%. Further, using table entire dataset, reached 96.66%. results promising process assessment