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Modified rotation forest ensemble classifier for medical diagnosis in decision support systems

Publication Type : Conference Paper

Publisher : Advances in Intelligent Systems and Computing

Source : Advances in Intelligent Systems and Computing, Springer Verlag, Volume 564, p.137-146 (2018)

Url :

ISBN : 9789811068744

Keywords : Artificial intelligence, Decision support systems, Decision trees, Diagnosis, Discriminant analysis, Ensemble algorithms, Intelligent computing, Learning algorithms, Learning systems, Linear discriminant analysis, Principal component analysis, Principle component analysis, Random forests, risk assessment, Rotation, Rotation forests

Campus : Amritapuri, Coimbatore

School : Department of Computer Science and Engineering, School of Computing, School of Engineering

Center : Computational Bioscience

Department : Computer Science

Year : 2018

Abstract : Decision support system (DSS) in medical diagnosis helps medical practitioners in assessing disease risks. The machine learning algorithms prove a better accuracy in predicting and diagnosing diseases. In this study, rotation forest algorithm is being used to analyse the performance of the classifiers in medical diagnosis. The study shows that rotation forest ensemble algorithm with random forest as base classifier outperformed random forest algorithm. In this study, we use linear discriminant analysis (LDA) in place of PCA for feature projection in modified rotation forest ensemble method for classification. The experimental result also reveals that LDA can provide better performance with rotation forest while comparing with PCA. The accuracies given by random forest, rotation forest and proposed modified rotation forest classifiers are 89%, 93% and 95%, respectively. © Springer Nature Singapore Pte Ltd. 2018.

Cite this Research Publication : Ani R., Jose, J., Wilson, M., and Deepa, O. S., “Modified rotation forest ensemble classifier for medical diagnosis in decision support systems”, in Advances in Intelligent Systems and Computing, 2018, vol. 564, pp. 137-146

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