Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches

Logistic regression was as good as machine learning for predicting major chronic diseases

(2020) Simon Nusinovici et al.   JOURNAL OF CLINICAL EPIDEMIOLOGY

A prediction nomogram for the 3-year risk of incident diabetes among Chinese adults

(2020) Yang Wu et al.   Scientific Reports

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

(2019) Evangelia Christodoulou et al.   JOURNAL OF CLINICAL EPIDEMIOLOGY

Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records

(2019) Binh P. Nguyen et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045

(2018) N.H. Cho et al.   DIABETES RESEARCH AND CLINICAL PRACTICE

Insulin resistance in obesity: an overview of fundamental alterations

(2018) Rocco Barazzoni et al.   Eating and Weight Disorders-Studies on Anorexia Bulimia and Obesity

Predicting Diabetes Mellitus With Machine Learning Techniques

(2018) Quan Zou et al.   Frontiers in Genetics

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project

(2017) Manal Alghamdi et al.   PLoS One

Machine Learning and Data Mining Methods in Diabetes Research

(2017) Ioannis Kavakiotis et al.   Computational and Structural Biotechnology Journal

Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study

(2016) Ariana E. Anderson et al.   JOURNAL OF BIOMEDICAL INFORMATICS

Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors

(2015) Narges Razavian et al.   Big Data

Age-Related Impairment of Pancreatic Beta-Cell Function: Pathophysiological and Cellular Mechanisms

(2014) Vincenzo De Tata   Frontiers in Endocrinology

Oral Cavity and Systemic Diseases—Diabetes Mellitus

(2011) Assya Krasteva et al.   BIOTECHNOLOGY & BIOTECHNOLOGICAL EQUIPMENT

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

(2011) Gary S Collins et al.   BMC Medicine

Recommendations for the Assessment and Reporting of Multivariable Logistic Regression in Transplantation Literature

(2010) A. C. Kalil et al.   AMERICAN JOURNAL OF TRANSPLANTATION

Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes

(2010) Wei Yu et al.   BMC Medical Informatics and Decision Making

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

Evaluation of Logistic Regression Reporting in Current Obstetrics and Gynecology Literature

(2010) Rafael T. Mikolajczyk et al.   OBSTETRICS AND GYNECOLOGY

Tools for Predicting the Risk of Type 2 Diabetes in Daily Practice

(2008) P. Schwarz et al.   HORMONE AND METABOLIC RESEARCH

Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes

(2008) Valeriya Lyssenko et al.   NEW ENGLAND JOURNAL OF MEDICINE