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- W2182870249 abstract "In today’s world, people get affected by many diseases which cannot be completely cured. Diabetes is one such disease and is now a big growing health problem. It leads to the risk of heart attack, kidney failure and renal disease. The techniques of data mining have been widely applied to extract knowledge from medical databases. In this paper, we evaluated the performance of knearest neighbor(kNN) algorithm for classification of Diabetes data. We considered the data imputation, scaling and normalization techniques to improve the accuracy of the classifier while using diabetes data, which may contain lot of missing values. We selected to explore KNN because, it is very simple and faster than most of the complex classification algorithms. To measure the performance, we used Accuracy and Error rate as the metrics. We found that data imputation method will not lead to higher accuracy; instead it will give correct accuracy for missing values and imputation along with a suitable data preprocessing method increases the accuracy." @default.
- W2182870249 created "2016-06-24" @default.
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- W2182870249 date "2012-01-01" @default.
- W2182870249 modified "2023-09-23" @default.
- W2182870249 title "IMPROVING THE PERFORMANCE OF K-NEAREST NEIGHBOR ALGORITHM FOR THE CLASSIFICATION OF DIABETES DATASET WITH MISSING VALUES" @default.
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