Abstract
Healthcare systems provide personalized services in wide spread domains to help patients in fitting themselves into their normal activities of life. This study is focused on the prediction of diabetes types of patients based on their personal and clinical information using a boosting ensemble technique that internally uses random committee classifier. To evaluate the technique, a real set of data containing 100 records is used. The prediction accuracy obtained is 81.0% based on experiments performed in Weka with 10-fold cross validation.
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Ali, R., Siddiqi, M.H., Idris, M., Kang, B.H., Lee, S. (2014). Prediction of Diabetes Mellitus Based on Boosting Ensemble Modeling. In: Hervás, R., Lee, S., Nugent, C., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services. UCAmI 2014. Lecture Notes in Computer Science, vol 8867. Springer, Cham. https://doi.org/10.1007/978-3-319-13102-3_6
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DOI: https://doi.org/10.1007/978-3-319-13102-3_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13101-6
Online ISBN: 978-3-319-13102-3
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