Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
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Title
Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
Authors
Keywords
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Journal
International Journal of Environmental Research and Public Health
Volume 18, Issue 14, Pages 7346
Publisher
MDPI AG
Online
2021-07-09
DOI
10.3390/ijerph18147346
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Related references
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- (2015) Narges Razavian et al. Big Data
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- (2014) Vincenzo De Tata Frontiers in Endocrinology
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- Recommendations for the Assessment and Reporting of Multivariable Logistic Regression in Transplantation Literature
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- A1C Between 5.7 and 6.4% as a Marker for Identifying Pre-Diabetes, Insulin Sensitivity and Secretion, and Cardiovascular Risk Factors: The Insulin Resistance Atherosclerosis Study (IRAS)
- (2010) C. Lorenzo et al. DIABETES CARE
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- Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes
- (2008) Valeriya Lyssenko et al. NEW ENGLAND JOURNAL OF MEDICINE
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