Abstract
Health care data are often huge and complex because it contains different attributes and also missing some values. Data mining techniques can be used to extract knowledge by constructing models from data such as diabetic patient datasets. This research aims at finding solutions to diagnose the disease by analyzing the patterns found in the dataset through data mining. In addition, the neural network approach is also used for classifying the existing diabetic patient data for predicting the patient’s disease based on the trained data that can lead to find the different level of diabetes in the patients. It is also compared with the association rule mining based approach for classification of data to validate the correctly classified cases.
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Acknowledgements
Authors are sincerely thankful to the Center for Clinical and Translational Research, Virginia Commonwealth University, a beneficiary of NIH CTSA grant UL1 TR00058 to provide raw dataset and also a beneficiary of the CERNER data. The work of the first author is supported by MHRD, India.
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Singh, P.P., Prasad, S., Das, B., Poddar, U., Choudhury, D.R. (2018). Classification of Diabetic Patient Data Using Machine Learning Techniques. In: Perez, G., Tiwari, S., Trivedi, M., Mishra, K. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 696. Springer, Singapore. https://doi.org/10.1007/978-981-10-7386-1_37
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DOI: https://doi.org/10.1007/978-981-10-7386-1_37
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