An Ensemble Model for Diabetes Diagnosis in Large-scale and Imbalanced Dataset

ABSTRACT

Diabetes is becoming a more and more serious health challenge worldwide with the yearly rising prevalence, especially in developing countries. The vast majority of diabetes are type 2 diabetes, which has been indicated that about 80% of type 2 diabetes complications can be prevented or delayed by timely detection. In this paper, we propose an ensemble model to precisely diagnose the diabetic on a large-scale and imbalance dataset. The dataset used in our work covers millions of people from one province in China from 2009 to 2015, which is highly skew. Results on the real-world dataset prove that our method is promising for diabetes diagnosis with a high sensitivity, F3 and G --- mean, i.e, 91.00%, 58.24%, 86.69%, respectively.

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Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by classification

    • Machine learning algorithms

      • Ensemble methods