Data mining approach helps in various extraction unit from large dataset. Mental health and brain statistics is an important body part which is directly connected with the human body. There are many symptoms which can observe from the mental health care dataset, especially with psychiatric dataset. There are many health diseases associated with such symptoms, i.e. Anxiety, Mood Disorder, Depression, etc, such as mental retardation, Alzheimer, dementia, and many other related with such symptoms. A proper classification and finding its efficiency is needed while dealing with different set of data. A classification of these diseases and analysis requirement makes it working for user understanding over disease. In this paper different classification algorithms are presented and classification is performed using J48 (C4.5), Random Forest (RF), and Random Tree (RT) approaches. The classification with precision, recall, ROC curve, and F-measure is taken in as computation parameter. An analysis shows that the Random tree based approach finds efficient result while comparing with J48 and Random Forest algorithm.