يعرض 1 - 13 نتائج من 13 نتيجة بحث عن '(( binary its derived optimization algorithm ) OR ( history data driven optimization algorithm ))', وقت الاستعلام: 0.48s تنقيح النتائج
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    Supplementary Material for: Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes حسب Yu Y. (3096192)

    منشور في 2022
    "…We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation. …"
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    List of data tables. حسب Mukhtar Ijaiya (18935122)

    منشور في 2025
    "…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …"
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    Flow chart of data source inclusion. حسب Mukhtar Ijaiya (18935122)

    منشور في 2025
    "…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …"
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    Predictive model-building process. حسب Mukhtar Ijaiya (18935122)

    منشور في 2025
    "…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …"
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    Comparison of models performance metrics. حسب Mukhtar Ijaiya (18935122)

    منشور في 2025
    "…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …"
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    Image 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.jpeg حسب Zhenze Zhang (22011422)

    منشور في 2025
    "…Predictors included demographics, comorbidities, laboratory parameters, vital signs, and disease severity scores. Missing data (<30%) were imputed using random forest. The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. …"
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    Table 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.docx حسب Zhenze Zhang (22011422)

    منشور في 2025
    "…Predictors included demographics, comorbidities, laboratory parameters, vital signs, and disease severity scores. Missing data (<30%) were imputed using random forest. The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. …"
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    Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants حسب Ahmed M. Alaa (5029781)

    منشور في 2019
    "…Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. …"
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