A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.

Journal: BMC medical informatics and decision making

Published Date: Nov 6, 2019

Abstract

BACKGROUND: Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of machine learning models in detecting at-risk patients using survey data (and laboratory results), and identify key variables within the data contributing to these diseases among the patients.

Authors

An Dinh

Department of Mathematics and Computer Science, Eastern Oregon University, La Grande, OR, USA.
Stacey Miertschin

Department of Mathematics and Statistics, Winona State University, Winona, MN, USA.
Amber Young

Department of Statistics, Purdue University, West Lafayette, IN, USA.
Somya D Mohanty

Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC, USA. sdmohant@uncg.edu.

Keywords

Cardiovascular Diseases Diabetes Mellitus Electronic Health Records Female Humans Logistic Models Machine Learning Male Nutrition Surveys Predictive Value of Tests ROC Curve Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (31694707)

A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.

Abstract

Authors

Keywords

External Resources

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