Open AccessPredicting hyperlipidemia using enhanced ensemble classifier
Author(s) -
K. S. Lakshmi,
G. Vadivu,
S. Subramanian
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.10693
Subject(s) - naive bayes classifier , classifier (uml) , computer science , artificial intelligence , decision tree , machine learning , bayes classifier , support vector machine , ensemble learning , probabilistic classification , data mining , pattern recognition (psychology)
Advancement in medical technology has resulted in bulk creation of electronic medical health records. These health records contain valuable data which are not fully utilized. Efficient usage of data mining techniques helps in discovering potentially relevant facts from medical records. Classification plays an important role in disease prediction. In this paper we developed a prediction model for predicting hyperlipidemia based on ensemble classification. Support Vector Machine, Naïve Bayes Classifier, KNN Classifier and Decision Tree method are combined for developing the ensemble classifier. Performance of each classifier is evaluated separately. An overall accuracy of 97.07% has been obtained by using ensemble approach which is better than the performance of each classifier.