A Data Mining Analysis Over Psychiatric Database for Mental Health Classification

Abstract

Data mining approach help 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 and especially with psychiatric dataset. There are many health disease associated with such symptoms i.e. Anxiety, Mood disorder, Depression etc. Diseases 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 disease and analysis requirement make it working for user understanding over disease. In this paper different classification algorithm is presented and classification is performed using J48 (C4.5), Random forest (RF) and Random Tree (RT) approach. 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 find efficient result while comparing with J48 and Random forest algorithm.