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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

207 related articles for article (PubMed ID: 36602674)

  • 1. 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]  

  • 2. Heterogeneous ensemble learning for enhanced crash forecasts - A frequentist and machine learning based stacking framework.
    Ahmad N; Wali B; Khattak AJ
    J Safety Res; 2023 Feb; 84():418-434. PubMed ID: 36868672
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Super Learner for Survival Data Prediction.
    Golmakani MK; Polley EC
    Int J Biostat; 2020 Feb; ():. PubMed ID: 32097120
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Can Hyperparameter Tuning Improve the Performance of a Super Learner?: A Case Study.
    Wong J; Manderson T; Abrahamowicz M; Buckeridge DL; Tamblyn R
    Epidemiology; 2019 Jul; 30(4):521-531. PubMed ID: 30985529
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study.
    Liao LD; Ferrara A; Greenberg MB; Ngo AL; Feng J; Zhang Z; Bradshaw PT; Hubbard AE; Zhu Y
    BMC Med; 2022 Sep; 20(1):307. PubMed ID: 36104698
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Classification of imbalanced data using machine learning algorithms to predict the risk of renal graft failures in Ethiopia.
    Mulugeta G; Zewotir T; Tegegne AS; Juhar LH; Muleta MB
    BMC Med Inform Decis Mak; 2023 May; 23(1):98. PubMed ID: 37217892
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. A Heterogeneous Ensemble Learning Method For Neuroblastoma Survival Prediction.
    Feng Y; Wang X; Zhang J
    IEEE J Biomed Health Inform; 2022 Apr; 26(4):1472-1483. PubMed ID: 33848254
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep Ensemble Machine Learning Framework for the Estimation of
    Yu W; Li S; Ye T; Xu R; Song J; Guo Y
    Environ Health Perspect; 2022 Mar; 130(3):37004. PubMed ID: 35254864
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The Balance Super Learner: A robust adaptation of the Super Learner to improve estimation of the average treatment effect in the treated based on propensity score matching.
    Pirracchio R; Carone M
    Stat Methods Med Res; 2018 Aug; 27(8):2504-2518. PubMed ID: 28339317
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Using a stacked ensemble learning framework to predict modulators of protein-protein interactions.
    Gao M; Zhao L; Zhang Z; Wang J; Wang C
    Comput Biol Med; 2023 Jul; 161():107032. PubMed ID: 37230018
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Exploiting ensemble learning to improve prediction of phospholipidosis inducing potential.
    Nath A; Sahu GK
    J Theor Biol; 2019 Oct; 479():37-47. PubMed ID: 31310757
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions.
    Ehwerhemuepha L; Danioko S; Verma S; Marano R; Feaster W; Taraman S; Moreno T; Zheng J; Yaghmaei E; Chang A
    Intell Based Med; 2021; 5():100030. PubMed ID: 33748802
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine learning versus traditional risk stratification methods in acute coronary syndrome: a pooled randomized clinical trial analysis.
    Gibson WJ; Nafee T; Travis R; Yee M; Kerneis M; Ohman M; Gibson CM
    J Thromb Thrombolysis; 2020 Jan; 49(1):1-9. PubMed ID: 31535314
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.