Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.

Journal: Journal of diabetes research

PMID: 33029536

Abstract

BACKGROUND: An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas.

Authors

Mingyue Xue

Hospital of Traditional Chinese Medicine Affiliated to the Fourth Clinical Medical College of Xinjiang Medical University, Urumqi, China.
Yinxia Su

College of Public Health, Xinjiang Medical University, Urumqi, China.
Chen Li

School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Shuxia Wang

Center of Health Management, The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China.
Hua Yao

Center of Health Management, The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China.

Keywords

Adult Aged Alcohol Drinking Algorithms Area Under Curve Asian People China Decision Making Decision Trees Diabetes Mellitus, Type 2 Female Humans Logistic Models Machine Learning Male Medically Underserved Area Middle Aged Odds Ratio Regression Analysis Risk Factors Smoking Surveys and Questionnaires Waist Circumference

External Resources

View on PubMed Access via DOI PubMed (33029536)

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.

Abstract

Authors

Keywords

External Resources

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