Publication Type:

Conference Paper


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





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


<p>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.</p>


cited By 0; Conference of International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2016 ; Conference Date: 23 December 2016 Through 25 December 2016; Conference Code:208989

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.