A Review of Methodological Approaches for Developing Diagnostic Algorithms for Diabetes Screening
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James M Muchira | Philimon N Gona | Suzanne Leveille | Laura L Hayman | Philimon N. Gona | L. Hayman | S. Leveille | James M. Muchira | P. Gona
[1] J. Veerman,et al. Population attributable fraction: names, types and issues with incorrect interpretation of relative risks , 2016, British Journal of Sports Medicine.
[2] Laura C Rosella,et al. A population-based risk algorithm for the development of diabetes: development and validation of the Diabetes Population Risk Tool (DPoRT) , 2010, Journal of Epidemiology & Community Health.
[3] H. Green,et al. Use of theoretical and conceptual frameworks in qualitative research. , 2014, Nurse researcher.
[4] E. Steyerberg,et al. Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research , 2013, PLoS medicine.
[5] Patrick Dattalo,et al. A Comparison of Discriminant Analysis and Logistic Regression , 1995 .
[6] J. Ioannidis,et al. Strengthening the reporting of genetic risk prediction studies: the GRIPS statement , 2011, Genetics in Medicine.
[7] E. Steyerberg,et al. Reporting and Methods in Clinical Prediction Research: A Systematic Review , 2012, PLoS medicine.
[8] M. Pencina,et al. On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data , 2011, Statistics in medicine.
[9] B. Tabachnick,et al. Using multivariate statistics, 5th ed. , 2007 .
[10] J. Sowers,et al. Diabetes and cardiovascular disease. , 1999, Diabetes care.
[11] K. Ananda Kumar,et al. Neural Networks In Medical And Healthcare , 2013 .
[12] Ivo D. Dinov,et al. Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data , 2016, GigaScience.
[13] Y. Jang,et al. Standards of Medical Care in Diabetes-2010 by the American Diabetes Association: Prevention and Management of Cardiovascular Disease , 2010 .
[14] David A. Sontag,et al. Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors , 2015, Big Data.
[15] K. V. N. Sunitha,et al. TreeNet analysis of human stress behavior using socio-mobile data , 2016, Journal of Big Data.
[16] O. Franco,et al. Impact of Healthy Lifestyle Factors on Life Expectancies in the US Population , 2018, Circulation.
[17] Roman Timofeev,et al. Classification and Regression Trees(CART)Theory and Applications , 2004 .
[18] T. Ahmed,et al. Simple risk score to detect rural Asian Indian (Bangladeshi) adults at high risk for type 2 diabetes , 2015, Journal of diabetes investigation.
[19] Nongyao Nai-arun,et al. Comparison of Classifiers for the Risk of Diabetes Prediction , 2015 .
[20] Wei Wang,et al. The study of statistical methods for evaluating the comparability of routine chemistry analytes among 3 routine laboratory measurement systems in China , 2016, SpringerPlus.
[21] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[22] J. Stoltzfus,et al. Logistic regression: a brief primer. , 2011, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.
[23] I. Vlahavas,et al. Machine Learning and Data Mining Methods in Diabetes Research , 2017, Computational and structural biotechnology journal.
[24] A. Harris,et al. REporting recommendations for tumour MARKer prognostic studies (REMARK) , 2005, British Journal of Cancer.
[25] Paige L Williams,et al. Development of a clinical guideline to predict undiagnosed diabetes in dental patients. , 2011, Journal of the American Dental Association.
[26] D. Sacks. A1C Versus Glucose Testing: A Comparison , 2011, Diabetes Care.
[27] F. Timmins. Nursing Research Generating and Assessing Evidence for Nursing Practice , 2013 .
[28] T. Nakayama,et al. Optimal Hemoglobin A1c Levels for Screening of Diabetes and Prediabetes in the Japanese Population , 2015, Journal of diabetes research.
[29] J. Tu,et al. Cardiovascular Disease Population Risk Tool (CVDPoRT): predictive algorithm for assessing CVD risk in the community setting. A study protocol , 2014, BMJ Open.
[30] Y. Skaik. Understanding and using sensitivity, specificity and predictive values , 2008, Indian journal of ophthalmology.
[31] J. Tuomilehto,et al. The validity of the Finnish Diabetes Risk Score for the prediction of the incidence of coronary heart disease and stroke, and total mortality , 2005, European journal of cardiovascular prevention and rehabilitation : official journal of the European Society of Cardiology, Working Groups on Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology.
[32] Azuraliza Abu Bakar,et al. Naïve bayes variants in classification learning , 2010, 2010 International Conference on Information Retrieval & Knowledge Management (CAMP).
[33] A. Vickers,et al. Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents , 2012, BMC Medical Research Methodology.
[34] J. S. Cramer. The Origins of Logistic Regression , 2002 .
[35] L. Smeeth,et al. Development and Validation of a Simple Risk Score for Undiagnosed Type 2 Diabetes in a Resource-Constrained Setting , 2016, Journal of diabetes research.
[36] Yvonne Vergouwe,et al. Prognosis and prognostic research: what, why, and how? , 2009, BMJ : British Medical Journal.
[37] Mohammad Khalilia,et al. Predicting disease risks from highly imbalanced data using random forest , 2011, BMC Medical Informatics Decis. Mak..
[38] Bo Zhang,et al. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study , 2008, The Lancet.
[39] Diana Adler,et al. Using Multivariate Statistics , 2016 .
[40] Viju Raghupathi,et al. Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.
[41] John R. Clark. The Social Science Research Network , 2002 .
[42] Bruce Ratner,et al. Variable selection methods in regression: Ignorable problem, outing notable solution , 2010 .
[43] G. Tutz,et al. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. , 2009, Psychological methods.
[44] Hui Li,et al. MTPGraph: A Data-Driven Approach to Predict Medical Risk Based on Temporal Profile Graph , 2016, 2016 IEEE Trustcom/BigDataSE/ISPA.
[45] S. U. Gulumbe,et al. Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis , 2015 .
[46] N. Wareham,et al. Estimating the population impact of screening strategies for identifying and treating people at high risk of cardiovascular disease: modelling study , 2010, BMJ : British Medical Journal.
[47] I. Stratton,et al. Development and validation of a Diabetes Risk Score for screening undiagnosed diabetes in Sri Lanka (SLDRISK) , 2016, BMC Endocrine Disorders.
[48] G. Collins,et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement , 2015, Annals of Internal Medicine.
[49] Xuesong Han,et al. Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence , 2017, PeerJ.
[50] J. Zhang,et al. Long-term effects of a randomised trial of a 6-year lifestyle intervention in impaired glucose tolerance on diabetes-related microvascular complications: the China Da Qing Diabetes Prevention Outcome Study , 2011, Diabetologia.
[51] K. Khunti,et al. he development and validation of the Portuguese risk score or detecting type 2 diabetes and impaired fasting glucose aura , 2013 .
[52] 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2018 , 2017, Diabetes Care.
[53] N. White,et al. Tool guide for lifestyle behavior change in a cardiovascular risk reduction program , 2013, Psychology research and behavior management.
[54] C. Rembold. Number needed to screen: development of a statistic for disease screening , 1998, BMJ.
[55] C. Florkowski. Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. , 2008, The Clinical biochemist. Reviews.