Accuracy of intelligent medical systems

Authors:
P. Povalej;M. Leni;M. Zorman;P. Kokol;D. Dinevski
Affiliations:
Laboratory for system design, Faculty for electrical engineering and computer science, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia;Laboratory for system design, Faculty for electrical engineering and computer science, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia;Laboratory for system design, Faculty for electrical engineering and computer science, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia;Laboratory for system design, Faculty for electrical engineering and computer science, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia;University of Maribor, Slomkov trg 15, 2000 Maribor, Slovenia
Venue:
Computer Methods and Programs in Biomedicine
Year:
2005

Citing 5
Cited 1

C4.5: programs for machine learning

C4.5: programs for machine learning
Technical Note: Bias in Information-Based Measures in Decision Tree Induction

Machine Learning
Estimating concept difficulty with cross entropy

Knowledge discovery and data mining
Bagging, boosting, and C4.S

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
No free lunch theorems for optimization

IEEE Transactions on Evolutionary Computation

The Enhancement of Security in Healthcare Information Systems

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Intelligent medical systems are a special kind of medical software in general, and just as any medical software system they should make accurate presumptions. However, accuracy of intelligent medical systems is highly dependent on various factors such as: choosing an appropriate basic method (i.e. decision trees, neural networks), induction method (i.e. purity measures) and appropriate support methods (i.e. discretization, pruning, boosting). In this paper we present the results of extensive research of the above alternatives on 54 UCI databases and their influence on the accuracy of decision trees, which constitute one of the most desirable forms of intelligent medical systems. We also introduce new hybrid purity measures that on some databases outperform other purity measures. The results presented here show that the selection of the right purity measure with the proper discretization method and application of the boosting method can really make a difference in terms of higher accuracy of induced decision trees. Thereafter choosing the appropriate factors that can increase the accuracy of the induced decision tree is a very demanding and time-consuming task.