Affiliations: [a] Department of Computer Science and Engineering, Biju Patnaik University of Technology, Rourkela, Odisha 769004, India | [b] Department of Computer Science and Engineering, NSRIT, Sontyam, Visakhapatnam 531173, India | [c] Department of Computer Science and Engineering, GIET, Gunupur, Odisha 765022, India
Correspondence:
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Corresponding author: Shiva Shankar Reddy, Department of Computer Science and Engineering, %****␣kes-23-kes190403_temp.tex␣Line␣25␣**** Biju Patnaik University of Technology, Rourkela, Odisha 769004, India. E-mail: [email protected].
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.
Keywords: Diabetes mellitus, data mining, adaboost, voting expert
