These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
170 related articles for article (PubMed ID: 35890927)
1. An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study. Laila UE; Mahboob K; Khan AW; Khan F; Taekeun W Sensors (Basel); 2022 Jul; 22(14):. PubMed ID: 35890927 [TBL] [Abstract][Full Text] [Related]
2. Prediction of diabetes disease using an ensemble of machine learning multi-classifier models. Abnoosian K; Farnoosh R; Behzadi MH BMC Bioinformatics; 2023 Sep; 24(1):337. PubMed ID: 37697283 [TBL] [Abstract][Full Text] [Related]
3. A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes. Arya M; Sastry G H; Motwani A; Kumar S; Zaguia A Front Public Health; 2021; 9():797877. PubMed ID: 35242738 [TBL] [Abstract][Full Text] [Related]
4. A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy. S K S; P A J Med Syst; 2017 Nov; 41(12):201. PubMed ID: 29124453 [TBL] [Abstract][Full Text] [Related]
5. Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023. Hossain S; Hasan MK; Faruk MO; Aktar N; Hossain R; Hossain K BMC Cardiovasc Disord; 2024 Apr; 24(1):214. PubMed ID: 38632519 [TBL] [Abstract][Full Text] [Related]
6. A hybrid super ensemble learning model for the early-stage prediction of diabetes risk. Doğru A; Buyrukoğlu S; Arı M Med Biol Eng Comput; 2023 Mar; 61(3):785-797. PubMed ID: 36602674 [TBL] [Abstract][Full Text] [Related]
7. Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data. Haq AU; Li JP; Khan J; Memon MH; Nazir S; Ahmad S; Khan GA; Ali A Sensors (Basel); 2020 May; 20(9):. PubMed ID: 32384737 [TBL] [Abstract][Full Text] [Related]
8. Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method-a Comparative Study. Verma AK; Pal S; Kumar S Appl Biochem Biotechnol; 2020 Feb; 190(2):341-359. PubMed ID: 31350666 [TBL] [Abstract][Full Text] [Related]
9. An intelligent warning model for early prediction of cardiac arrest in sepsis patients. Layeghian Javan S; Sepehri MM; Layeghian Javan M; Khatibi T Comput Methods Programs Biomed; 2019 Sep; 178():47-58. PubMed ID: 31416562 [TBL] [Abstract][Full Text] [Related]
10. An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI. Kibria HB; Nahiduzzaman M; Goni MOF; Ahsan M; Haider J Sensors (Basel); 2022 Sep; 22(19):. PubMed ID: 36236367 [TBL] [Abstract][Full Text] [Related]
11. Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study. Serviá L; Montserrat N; Badia M; Llompart-Pou JA; Barea-Mendoza JA; Chico-Fernández M; Sánchez-Casado M; Jiménez JM; Mayor DM; Trujillano J BMC Med Res Methodol; 2020 Oct; 20(1):262. PubMed ID: 33081694 [TBL] [Abstract][Full Text] [Related]
12. A new hybrid ensemble machine-learning model for severity risk assessment and post-COVID prediction system. Shakhovska N; Yakovyna V; Chopyak V Math Biosci Eng; 2022 Apr; 19(6):6102-6123. PubMed ID: 35603393 [TBL] [Abstract][Full Text] [Related]
13. An ensemble learning approach for diabetes prediction using boosting techniques. Ganie SM; Pramanik PKD; Bashir Malik M; Mallik S; Qin H Front Genet; 2023; 14():1252159. PubMed ID: 37953921 [No Abstract] [Full Text] [Related]
14. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. Dinh A; Miertschin S; Young A; Mohanty SD BMC Med Inform Decis Mak; 2019 Nov; 19(1):211. PubMed ID: 31694707 [TBL] [Abstract][Full Text] [Related]
15. Supervised Machine Learning-Based Models for Predicting Raised Blood Sugar. Owess MM; Owda AY; Owda M; Massad S Int J Environ Res Public Health; 2024 Jun; 21(7):. PubMed ID: 39063417 [TBL] [Abstract][Full Text] [Related]
16. Early warning of telecom enterprise customer churn based on ensemble learning. Zhou Y; Chen W; Sun X; Yang D PLoS One; 2023; 18(10):e0292466. PubMed ID: 37819986 [TBL] [Abstract][Full Text] [Related]
17. An automated approach to predict diabetic patients using KNN imputation and effective data mining techniques. Altamimi A; Alarfaj AA; Umer M; Alabdulqader EA; Alsubai S; Kim TH; Ashraf I BMC Med Res Methodol; 2024 Sep; 24(1):221. PubMed ID: 39333904 [TBL] [Abstract][Full Text] [Related]
18. Chronic kidney disease prediction using boosting techniques based on clinical parameters. Ganie SM; Dutta Pramanik PK; Mallik S; Zhao Z PLoS One; 2023; 18(12):e0295234. PubMed ID: 38039306 [TBL] [Abstract][Full Text] [Related]
19. Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques. Li J; Chen Q; Hu X; Yuan P; Cui L; Tu L; Cui J; Huang J; Jiang T; Ma X; Yao X; Zhou C; Lu H; Xu J Int J Med Inform; 2021 May; 149():104429. PubMed ID: 33647600 [TBL] [Abstract][Full Text] [Related]
20. Machine learning algorithm to evaluate risk factors of diabetic foot ulcers and its severity. Nanda R; Nath A; Patel S; Mohapatra E Med Biol Eng Comput; 2022 Aug; 60(8):2349-2357. PubMed ID: 35751828 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]