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For: Silva K, Lee WK, Forbes A, Demmer RT, Barton C, Enticott J. Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis. Int J Med Inform 2020;143:104268. [PMID: 32950874 DOI: 10.1016/j.ijmedinf.2020.104268] [Cited by in Crossref: 25] [Cited by in F6Publishing: 14] [Article Influence: 8.3] [Reference Citation Analysis]
Number Citing Articles
1 Madhav G, Goel S. Type 2 Diabetes Prediction Using Machine Learning and Validation Using Weka Tool. International Conference on Innovative Computing and Communications 2023. [DOI: 10.1007/978-981-19-3679-1_23] [Reference Citation Analysis]
2 Shin J, Lee J, Ko T, Lee K, Choi Y, Kim H. Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness. JPM 2022;12:1899. [DOI: 10.3390/jpm12111899] [Reference Citation Analysis]
3 Olusanya MO, Ogunsakin RE, Ghai M, Adeleke MA. Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach. Int J Environ Res Public Health 2022;19. [PMID: 36361161 DOI: 10.3390/ijerph192114280] [Reference Citation Analysis]
4 Seto H, Oyama A, Kitora S, Toki H, Yamamoto R, Kotoku J, Haga A, Shinzawa M, Yamakawa M, Fukui S, Moriyama T. Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data. Sci Rep 2022;12:15889. [PMID: 36220875 DOI: 10.1038/s41598-022-20149-z] [Reference Citation Analysis]
5 Glanz V, Dudenkov V, Velikorodny A. Development and validation of a type 2 diabetes machine learning classification model for clinical decision support framework.. [DOI: 10.1101/2022.10.08.511400] [Reference Citation Analysis]
6 Glanz V, Dudenkov V, Velikorodny A. Development and validation of a type 2 diabetes machine learning classification model for clinical decision support framework.. [DOI: 10.21203/rs.3.rs-2033259/v1] [Reference Citation Analysis]
7 Song Z, Luo W, Huang B, Cao Y, Jiang R. A new predictive model for the concurrent risk of diabetic retinopathy in type 2 diabetes patients and the effect of metformin on amino acids. Front Endocrinol 2022;13:985776. [DOI: 10.3389/fendo.2022.985776] [Reference Citation Analysis]
8 Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022;166:104855. [PMID: 35998421 DOI: 10.1016/j.ijmedinf.2022.104855] [Reference Citation Analysis]
9 Zhang T, Nikouline A, Lightfoot D, Nolan B. Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review. Ann Emerg Med 2022:S0196-0644(22)00335-3. [PMID: 35842343 DOI: 10.1016/j.annemergmed.2022.05.011] [Reference Citation Analysis]
10 Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, Moons KGM, Collins GS. Risk of bias of prognostic models developed using machine learning: a systematic review in oncology. Diagn Progn Res 2022;6:13. [PMID: 35794668 DOI: 10.1186/s41512-022-00126-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Liu Q, Zhang M, He Y, Zhang L, Zou J, Yan Y, Guo Y. Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques. JPM 2022;12:905. [DOI: 10.3390/jpm12060905] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
12 Baskozos G, Themistocleous AC, Hebert HL, Pascal MMV, John J, Callaghan BC, Laycock H, Granovsky Y, Crombez G, Yarnitsky D, Rice ASC, Smith BH, Bennett DLH. Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts. BMC Med Inform Decis Mak 2022;22. [DOI: 10.1186/s12911-022-01890-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
13 Buyrukoğlu S, Akbaş A. Machine Learning based Early Prediction of Type 2 Diabetes: A New Hybrid Feature Selection Approach using Correlation Matrix with Heatmap and SFS. Balkan Journal of Electrical and Computer Engineering 2022;10:110-117. [DOI: 10.17694/bajece.973129] [Reference Citation Analysis]
14 Swislocki AL. Glucose Trajectory: More than Changing Glucose Tolerance with Age? Metabolic Syndrome and Related Disorders. [DOI: 10.1089/met.2021.0093] [Reference Citation Analysis]
15 Xiong Y, Ma Y, Ruan L, Li D, Lu C, Huang L; National Traditional Chinese Medicine Medical Team. Comparing different machine learning techniques for predicting COVID-19 severity. Infect Dis Poverty 2022;11:19. [PMID: 35177120 DOI: 10.1186/s40249-022-00946-4] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
16 Hong N, Park Y, You SC, Rhee Y. AIM in Endocrinology. Artificial Intelligence in Medicine 2022. [DOI: 10.1007/978-3-030-64573-1_328] [Reference Citation Analysis]
17 Ming W, He Z. Different Machine Learning Algorithms Involved in Glucose Monitoring to Prevent Diabetes Complications and Enhanced Diabetes Mellitus Management. Springer Series on Bio- and Neurosystems 2022. [DOI: 10.1007/978-3-030-99728-1_11] [Reference Citation Analysis]
18 Fregoso-Aparicio L, Noguez J, Montesinos L, García-García JA. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetol Metab Syndr 2021;13:148. [PMID: 34930452 DOI: 10.1186/s13098-021-00767-9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
19 Gautier T, Ziegler LB, Gerber MS, Campos-Náñez E, Patek SD. Artificial intelligence and diabetes technology: A review. Metabolism 2021;124:154872. [PMID: 34480920 DOI: 10.1016/j.metabol.2021.154872] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
20 Vettoretti M, Di Camillo B. A Variable Ranking Method for Machine Learning Models with Correlated Features: In-Silico Validation and Application for Diabetes Prediction. Applied Sciences 2021;11:7740. [DOI: 10.3390/app11167740] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
21 Alix G, Huang H, Guergachi A, Keshavjee K, Gao X. An Online Risk Tool for Predicting Type 2 Diabetes Mellitus. Diabetology 2021;2:123-129. [DOI: 10.3390/diabetology2030011] [Reference Citation Analysis]
22 Hyun MK, Lee JW, Ko SH, Hwang JS. Improving Glycemic Control in Type 2 Diabetes Using Mobile Applications and e-Coaching: A Mixed Treatment Comparison Network Meta-Analysis. J Diabetes Sci Technol 2021;:19322968211010153. [PMID: 33980055 DOI: 10.1177/19322968211010153] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
23 De Silva K, Lim S, Mousa A, Teede H, Forbes A, Demmer RT, Jönsson D, Enticott J. Nutritional markers of undiagnosed type 2 diabetes in adults: Findings of a machine learning analysis with external validation and benchmarking. PLoS One 2021;16:e0250832. [PMID: 33951067 DOI: 10.1371/journal.pone.0250832] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
24 Li J, Zhou Z, Dong J, Fu Y, Li Y, Luan Z, Peng X. Predicting breast cancer 5-year survival using machine learning: A systematic review. PLoS One 2021;16:e0250370. [PMID: 33861809 DOI: 10.1371/journal.pone.0250370] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 9.5] [Reference Citation Analysis]
25 Hong N, Park Y, You SC, Rhee Y. AIM in Endocrinology. Artificial Intelligence in Medicine 2021. [DOI: 10.1007/978-3-030-58080-3_328-1] [Reference Citation Analysis]
26 Zhang Z, Yang L, Han W, Wu Y, Zhang L, Gao C, Kui J, Liu Y, Wu H. Machine Learning Prediction Models for Gestational Diabetes Mellitus: A meta- analysis (Preprint). Journal of Medical Internet Research. [DOI: 10.2196/26634] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
27 Zhang Z, Yang L, Han W, Wu Y, Zhang L, Gao C, Jiang K, Liu Y, Wu H. Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis (Preprint).. [DOI: 10.2196/preprints.26634] [Reference Citation Analysis]