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A data mining scheme for detection and classification of diabetes mellitus using voting expert strategy.
- Source :
-
International Journal of Knowledge Based Intelligent Engineering Systems . 2019, Vol. 23 Issue 2, p103-108. 6p. - Publication Year :
- 2019
-
Abstract
- In this work, an efficient scheme has been proposed for the computer-aided detection of the wide-spread disease diabetes. This scheme involves certain data mining techniques for the purpose of detecting the chances of diabetes by looking into a patient's medical record. This work attempts to classify the nature of diabetes (Type-I and Type-II) as well. It also tries to determine the level of risk associated presently with the affected patient. Four different algorithms namely decision tree, Naive Bayes, support vector machine (SVM), and Adaboost-M1 have been used for the purpose of labeling the records as either diabetic or non-diabetic. A comparison strategy is then followed to adopt the best scheme among these through the voting expert. The proposed work gives satisfactory diagnosis result when compared to the ground-truth data. Overall accuracy rate of 95% is achieved through k-fold cross-validation (k = 10) method. Comparison of the proposed work with other state-of-the-art schemes has also been performed that favors the said work. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13272314
- Volume :
- 23
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- International Journal of Knowledge Based Intelligent Engineering Systems
- Publication Type :
- Academic Journal
- Accession number :
- 137798163
- Full Text :
- https://doi.org/10.3233/KES-190403