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- W2888287730 abstract "Medical decision making is characterized by an exponential evolution of knowledge. With the increasing trend of healthcare applications in medical domain, disease prediction has become the centre of research. In an actual risk assessment process, the discovery of a disease prediction model is essential for patients and physicians. To estimate these risks, enormous classification and prediction algorithms have been developed in the field of data mining (DM). Recently, the World Health Organization has reported type II diabetes as the major cause for complications such as blindness, amputation and kidney failure. So, this paper has aimed to compare the performance of five classification approaches namely ant-miner, CN2, RBF network, Adaboost and Bagging for the prediction of diabetes mellitus. These classification approaches have been tested with three sets of type II diabetes datasets [PIMA, US, AIM'94] obtained from the UCI machine learning repository in terms of sensitivity, specificity, F-score, accuracy, chance agreement and kappa. The results indicate that ant-miner algorithm has achieved the highest kappa value of 0.982 which indicates a perfect level of agreement between the medical expert's opinion and the corresponding classification approach." @default.
- W2888287730 created "2018-08-31" @default.
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- W2888287730 date "2017-12-01" @default.
- W2888287730 modified "2023-09-23" @default.
- W2888287730 title "Performance Analysis of Classification Approaches for the Prediction of Type II Diabetes" @default.
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- W2888287730 doi "https://doi.org/10.1109/icoac.2017.8441197" @default.
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