Diagnosis and predictive clinical and para-clinical cutoffs for diabetes complications in Lur and Lak populations of Iran; a ROC curve analysis to design a regional guideline

Babak Khodadadi ^1,2, Nazanin Mousavi ^1,2, Mahshad Mousavi ^1,2, Parastoo Baharvand ³, Seyyed Amir Yasin Ahmadi ⁴ *

Cited by CrossRef: 5

1- Maniruzzaman M, Islam M, Rahman M, Hasan M, Shin J. Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2021;15(5):102263 [Crossref]

2- Ahmadi S, Shirzadegan R, Mousavi N, Farokhi E, Soleimaninejad M, Jafarzadeh M, Papanas N. Designing a Logistic Regression Model for a Dataset to Predict Diabetic Foot Ulcer in Diabetic Patients: High-Density Lipoprotein (HDL) Cholesterol Was the Negative Predictor. Journal of Diabetes Research. 2021;2021:1 [Crossref]

3- Alfian G, Syafrudin M, Fitriyani N, Anshari M, Stasa P, Svub J, Rhee J. Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors. Mathematics. 2020;8(9):1620 [Crossref]

4- Ahmadi S, Shahsavar F, Anbari K, Rezaian J. An introduction to the role of immunology in medical anthropology and molecular epidemiology. Biomedicine & Pharmacotherapy. 2019;109:2203 [Crossref]