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: 32384737)
1. 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]
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]
4. Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation. Li Q; Liu Y; Zhu J; Chen Z; Liu L; Yang S; Zhu G; Zhu B; Li J; Jin R; Tao J; Chen L JMIR Mhealth Uhealth; 2021 Sep; 9(9):e24402. PubMed ID: 34473067 [TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models. Jalali-Najafabadi F; Stadler M; Dand N; Jadon D; Soomro M; Ho P; Marzo-Ortega H; Helliwell P; Korendowych E; Simpson MA; Packham J; Smith CH; Barker JN; McHugh N; Warren RB; Barton A; Bowes J; ; Sci Rep; 2021 Dec; 11(1):23335. PubMed ID: 34857774 [TBL] [Abstract][Full Text] [Related]
8. Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease. Khalid H; Khan A; Zahid Khan M; Mehmood G; Shuaib Qureshi M Comput Intell Neurosci; 2023; 2023():9266889. PubMed ID: 36959840 [TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. KFPredict: An ensemble learning prediction framework for diabetes based on fusion of key features. Qi H; Song X; Liu S; Zhang Y; Wong KKL Comput Methods Programs Biomed; 2023 Apr; 231():107378. PubMed ID: 36731312 [TBL] [Abstract][Full Text] [Related]
12. Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset. Velusamy D; Ramasamy K Comput Methods Programs Biomed; 2021 Jan; 198():105770. PubMed ID: 33027698 [TBL] [Abstract][Full Text] [Related]
13. Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Ozcift A; Gulten A Comput Methods Programs Biomed; 2011 Dec; 104(3):443-51. PubMed ID: 21531475 [TBL] [Abstract][Full Text] [Related]
14. Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics. Sahid MA; Babar MUH; Uddin MP PLoS One; 2024; 19(5):e0300785. PubMed ID: 38753669 [TBL] [Abstract][Full Text] [Related]
15. A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work. Salman OH; Taha Z; Alsabah MQ; Hussein YS; Mohammed AS; Aal-Nouman M Comput Methods Programs Biomed; 2021 Sep; 209():106357. PubMed ID: 34438223 [TBL] [Abstract][Full Text] [Related]
16. A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification. Mandal M; Singh PK; Ijaz MF; Shafi J; Sarkar R Sensors (Basel); 2021 Aug; 21(16):. PubMed ID: 34451013 [TBL] [Abstract][Full Text] [Related]
17. A diabetes prediction model based on Boruta feature selection and ensemble learning. Zhou H; Xin Y; Li S BMC Bioinformatics; 2023 Jun; 24(1):224. PubMed ID: 37264332 [TBL] [Abstract][Full Text] [Related]
18. Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation. García-Domínguez A; Galván-Tejada CE; Magallanes-Quintanar R; Gamboa-Rosales H; Curiel IG; Peralta-Romero J; Cruz M J Diabetes Res; 2023; 2023():9713905. PubMed ID: 37404324 [TBL] [Abstract][Full Text] [Related]
19. Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective. Olisah CC; Smith L; Smith M Comput Methods Programs Biomed; 2022 Jun; 220():106773. PubMed ID: 35429810 [TBL] [Abstract][Full Text] [Related]
20. A proposed technique for predicting heart disease using machine learning algorithms and an explainable AI method. El-Sofany H; Bouallegue B; El-Latif YMA Sci Rep; 2024 Oct; 14(1):23277. PubMed ID: 39375427 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]