Enhancing Decision Tree with AdaBoost for Predicting Schizophrenia Readmission

Abstract:

A psychiatric readmission is argued to be an adverse outcome because it is costly and occurs when relapse to the illness is so severe. An analysis of systematic models in readmission data can provide useful insight into the quicker and sicker patients with schizophrenia. This research aims to develop and investigate schizophrenia readmission prediction models using data mining techniques including decision tree, Random Tree, Random Forests, AdaBoost, Bagging and a combination of AdaBoost with decision tree, AdaBoost with Random Tree, AdaBoost with Random Forests, Bagging with decision tree, Bagging with Random Tree and Bagging with Random Forests. The experimental results successfully showed that AdaBoost with decision tree has the highest precision, recall and F-measure up to 98.11%, 98.79% and 98.41%, respectively.

• Periodical: Advanced Materials Research (Volumes 931-932)
• Pages: 1467-1471
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.931-932.1467
• Online since: May 2014
• Authors: Jaree Thongkam*, Vatinee Sukmak
• Keywords: Classification, Machine Learning, Schizophrenia Readmission

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