يعرض 1 - 9 نتائج من 9 نتيجة بحث عن 'binary aging model optimization algorithm', وقت الاستعلام: 0.14s تنقيح النتائج
  1. 1

    ROC curves for the test set of four models. حسب Meng Cao (105914)

    منشور في 2025
    "…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…"
  2. 2

    SHAP bar plot. حسب Meng Cao (105914)

    منشور في 2025
    "…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…"
  3. 3

    Sample screening flowchart. حسب Meng Cao (105914)

    منشور في 2025
    "…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…"
  4. 4

    Descriptive statistics for variables. حسب Meng Cao (105914)

    منشور في 2025
    "…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…"
  5. 5

    SHAP summary plot. حسب Meng Cao (105914)

    منشور في 2025
    "…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…"
  6. 6

    Display of the web prediction interface. حسب Meng Cao (105914)

    منشور في 2025
    "…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…"
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    An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach حسب Sanaa Badr (20628838)

    منشور في 2025
    "…Binary classification models were developed to classify cases into two groups: those transferring two or fewer embryos and those transferring three or four. …"
  9. 9

    Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx حسب Ali Nabavi (21097424)

    منشور في 2025
    "…., blood lead and cadmium levels) were analyzed as features, with hearing loss status—defined as a pure-tone average threshold exceeding 25 dB HL across 500, 1,000, 2000, and 4,000 Hz in the better ear—serving as the binary outcome. Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …"