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Objectives
Prediabetes is a major epidemic and is associated with adverse cardio-cerebrovascular outcomes. Early identification of patients who will develop rapid progression of atherosclerosis could be beneficial for improved risk

Objectives
Prediabetes is a major epidemic and is associated with adverse cardio-cerebrovascular outcomes. Early identification of patients who will develop rapid progression of atherosclerosis could be beneficial for improved risk stratification. In this paper, we investigate important factors impacting the prediction, using several machine learning methods, of rapid progression of carotid intima-media thickness in impaired glucose tolerance (IGT) participants.
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    Date Created
    • 2016-09-05
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  • Text
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    Identifier
    • Digital object identifier: 10.1186/s13637-016-0049-6
    • Identifier Type
      International standard serial number
      Identifier Value
      1687-4145
    • Identifier Type
      International standard serial number
      Identifier Value
      1687-4153
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    Hu, X., Reaven, P. D., Saremi, A., Liu, N., Abbasi, M. A., Liu, H., & Migrino, R. Q. (2016). Machine learning to predict rapid progression of carotid atherosclerosis in patients with impaired glucose tolerance. EURASIP Journal on Bioinformatics and Systems Biology, 2016(1). doi:10.1186/s13637-016-0049-6

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