A hybrid reduced approach to handle missing values in type 2 diabetes prediction

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2016-05-06

Authors

You, Xinqi

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Abstract

Diabetes gains more attention among medical institutions and health care organizations as the increasing trend of diabetes around the world. In the United States, 29.1 million people or 9.3% of U.S. population are diagnosed with diabetes. About 86 million people are categorized as pre-diabetes and 15-30% of them will develop diabetes within 5 years. To tackle this challenge, National Diabetes Prevention Program (DPP) was introduced in 2002 and it reduces risk of diabetes by 58% through lifestyle change program. In order to help select a better group of prediabetes for intervention and maximize the cost-effectiveness of the program, we propose a Hybrid Reduced approach to handle missing values when predicting type 2 diabetes. This approach deals with 4 challenges in electronic medical records: missing values, missing not at random, class imbalance and predicting at a longer window (2-year). We select three ensemble predictive models: AdaBoost.M1, Gradient Boosting and Extremely Randomized Trees and apply this approach across 7 years to assess its robustness. The Hybrid Reduced approach includes two sub-approaches: Hybrid Reduced Organic and Hybrid Reduced Imputed. Throughout the experiments, Hybrid Reduced Imputed is the best performer and achieves a 5-7% improvement in precision. By simply using this approach, we could save $278 million for healthcare and improve people’s health condition

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