Performance Study on Diabetic Disease Prediction Using Classification Techniques
DOI:
https://doi.org/10.26438/ijcse/v6i2.130135Keywords:
Data mining, Diabetes disease, JRip, PART, Random Tree, Weighted Classifier, classification, WEKA toolAbstract
Data mining techniques can be used by Health organizations to identify the diseases like heart, tumor, diabetic, liver and thyroid disease using symptoms as parameters. Diabetic disorder is one of the growing diseases worldwide currently faced by people because of modified life style. Valuable data can be observed from application of knowledge mining techniques in the fitness care system particularly in Diabetic Disease. In this direction, this research paper studies the performance of three classifier algorithms available, namely JRip, PART and Random Tree using WEKA tool and proposed a new algorithm Weighted Classifier to classify the data a diabetic data set. The objective of this research is to classify data, assist the people by extracting useful knowledge from classified data and identify the efficient algorithm to best prediction of disease. From the experimental analysis, it is concluded that weighted Classifier is the effective algorithm for classification accuracy. The result will help doctors in a diagnosis process.
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