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Diabetic Medical Data Classification using Machine Learning Algorithms


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1 School of Computer Science and Engineering, VIT University, Vellore-632014, India
     

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Data mining is the process of analyzing data from different perspectives and summarizing it into a useful information. In this paper we propose a different classification algorithm to identify the accuracy on diabetic data sets. The diabetic person has risk and leads to other disease such as blood vessel damage, blindness, heart diseases, nerve damage and kidney diseases. Diabetics also classified as two types such as type insulin diabetes and non-insulin dependent, diabetes is a disease in which the blood glucose increases which is due to the defects of secretion of insulin, or its action or both. Diabetes is a prolonged medical disease. In diabetes the cells of person produce insufficient amount of insulin or defective insulin or may insulin or may unable use insulin properly and efficiently that further leads to hyperglycemia and type-2 diabetes. We are proposing an efficient two level for classifying data. During initial phase we use training data for analyzing the optimality of dataset then new dataset is formed as optimal training dataset now we apply our classification mechanism on new diabetic datasets. The data mining methods and techniques will be explored to identify suitable methods and techniques for efficient classification on diabetic data set and in mining it in useful patterns.

Keywords

Data Mining, Diabetic Dataset, Classification, Naive Bayes Classification, Random Forest.
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  • Rahman, R. M. and Afroz, F. Comparison of various classification techniques using different data mining tools for diabetes diagnosis. Journal of Software Engineering and Applications, 2013; 6(03): 85-97.
  • R. S. Kamath, Weka Approach for Exploration Mining in Diabetic Patients Database, Chatrapati Shahu Institute of Business Education and Research Kolhapur,India.2013
  • Labatut, V and Cherifi, H. Evaluation of performance measures for classifiers comparison. Ubiquitous Computing and Communication Journal, 2011; 6, 2011:21-34
  • Kumari, M., Vohra, R., and Arora, A. Prediction of Diabetes Using Bayesian Network, International Journal of Computer Science and Information Technologies, 2014; 5(4) : 5174-5178.
  • Keerthana, G., and Srividhya, V. (2014). Performance Enhancement of Classifiers using Integration of Clustering and Classification Techniques. International Journal of Computer Science Engineering 2014;3(3) : 200-203.
  • Marom, N. D., Rokach, L., and Shmilovici, A. Using the confusion matrix for improving ensemble classifiers. In 26th Convention of Electrical and Electronics Engineers in Israel (IEEEI), 2010:555-559.

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  • Diabetic Medical Data Classification using Machine Learning Algorithms

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Authors

K. Naresh
School of Computer Science and Engineering, VIT University, Vellore-632014, India
N. Prabakaran
School of Computer Science and Engineering, VIT University, Vellore-632014, India
R. Kannadasan
School of Computer Science and Engineering, VIT University, Vellore-632014, India
P. Boominathan
School of Computer Science and Engineering, VIT University, Vellore-632014, India

Abstract


Data mining is the process of analyzing data from different perspectives and summarizing it into a useful information. In this paper we propose a different classification algorithm to identify the accuracy on diabetic data sets. The diabetic person has risk and leads to other disease such as blood vessel damage, blindness, heart diseases, nerve damage and kidney diseases. Diabetics also classified as two types such as type insulin diabetes and non-insulin dependent, diabetes is a disease in which the blood glucose increases which is due to the defects of secretion of insulin, or its action or both. Diabetes is a prolonged medical disease. In diabetes the cells of person produce insufficient amount of insulin or defective insulin or may insulin or may unable use insulin properly and efficiently that further leads to hyperglycemia and type-2 diabetes. We are proposing an efficient two level for classifying data. During initial phase we use training data for analyzing the optimality of dataset then new dataset is formed as optimal training dataset now we apply our classification mechanism on new diabetic datasets. The data mining methods and techniques will be explored to identify suitable methods and techniques for efficient classification on diabetic data set and in mining it in useful patterns.

Keywords


Data Mining, Diabetic Dataset, Classification, Naive Bayes Classification, Random Forest.

References