An Ensemble Technique for Early Prediction of Type 2 Diabetes Mellitus – A Normalization Approach
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Abstract
Diabetic disease in particular originates due to the presence of blood sugar higher than at normal level. This is mainly due to the inadequate production of insulin. In recent days, it has been noticed that in all parts of the world, there has been an increasing number of people affected by diabetes. In the coming days, it is clear that this problem needs to be given greater importance in ensuring a reduction in the average diabetes affecting people. Many researchers carried out comprehensive research recently in the data mining platform to assess the accuracy of diabetic disease prediction at an early stage. The proposed ensemble model incorporates J48, NBTree, RandomForest, SimpleCART, and RandomTree and it is focussed on improving performance parameters such as accuracy, precision, f-measure, ROC, PRC and computational time. The conclusion shows that obtained accuracy is 4.03% higher than other standalone techniques was obtained using the proposed ensemble model.
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