Methods for Predicting Type 2 Diabetes
نویسندگان
چکیده
Diabetes Mellitus type 2 (T2DM) is the most common form of diabetes [WHO (2008)]. More than 29 million people in the United States are affected by T2DM and another 86 million are in a state of prediabetes, a condition that exhibits high risk to progress into diabetes [NIH (2014)]. Many T2DM cases can be prevented or avoided by improved awareness and lifestyle adjustments [NIH (2014)]. Our project aims to improve T2DM diagnosis methodologies using supervised machine learning algorithms trained on data from electronic medical records (EMR). Specifically, SVM, Adaptive Boosting with Decision Trees, Random Forests, and Logistic Regression were used to build models for predicting T2DM.
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