Analysis and Comparison of Models for Classification of Diabetic Disease
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 5
Abstract
Diagnosis of health condition is very challenging task in medical science. In healthcare, due to large amount of data to extract the useful information and knowledge is very essential. Machine learning techniques play major role and beneficial in health care industry. Classification technique is one of the important machine learning technique which is used as decision maker in real word problem. In this research work, we have used various classification techniques to classify the diabetic and non diabetic disease. We have used Tanagra and WEKA data mining software to analysis of diabetic patient using Indian Liver Patient Diabetic (ILPD) data set. We have compared the performance of models in terms of accuracy, true positive rate (TPR) and true negative rate (TNR) using both data mining software with 10-fold cross validation. Multilayer Perceptron (MLP) achieved better accuracy as 76.18% in case of Tanagra data mining tool while SVM achieved better accuracy as 77.34% in case of WEKA data mining tool. Finally, we conclude that accuracy of models is varying from one tool to another tool.
Authors and Affiliations
Ashutosh Dwivedi, Amit Kumar Dewangan, A. K. Shrivas
Keywords
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