Human is becoming busier, and the rush hours can cause physical and mental stresses. In many countries, it is found that the patients in neurological department are increasing randomly. However, the numbers of doctors are not sufficient to serve the patients. Furthermore, at times the doctors need other doctors' opinion in supporting the decision making. This paper investigates the potential developing of an expert system for headache detection.
Ensemble classifier systems are considered as one of the most promising in medical data classification and the performance of deceision tree classifier can be increased by the ensemble method as it is proven to be better than single classifiers. However, in a ensemble settings the performance depends on the selection of suitable base classifier. This research employed two prominent esemble s namely Adaboost and Bagging with base classifiers such as Random Forest, Random Tree, j48, j48grafts and Logistic Model Regression (LMT) that have been selected independently. The empirical study shows that the performance varries when different base classifiers are selected and even some places overfitting issue also been noted. The evidence shows that ensemble decision tree classfiers using Adaboost and Bagging improves the performance of selected medical data sets.
LITERATURE REVIEWEnsemble methods are considered as a more advanced data mining technique where multiple classifiers (in this study Decision Tree Classifiers abbreviate as "DTC") are combined to produce better predictions and more robust methods [8]. An ensemble classifier is a classifier that combines multiple base classifiers for final classification (see Fig. 1). In this research the focus remained on decision tree ensemble.
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