A Comparison efficiency of classification of diabetic patients using data transformation techniques for data mining techniques

Abstract

The objective of this study was to built a classification model for diabetes patients from the transformed datasets using Min-Max, Mean, Z-score and Root formats to compare whether the transformed data were diabetic. Which is suitable for the classification technique that provides the best classification accuracy? By comparing the model efficiency of 4 types of data mining techniques, namely, Neural Network, Decision tree and k – nearest neighbor. From the experiment, it was found that the neural network had the highest efficiency in data classification accuracy is 75.13%.