بدائل البحث:
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), wolf optimization (توسيع البحث)
aging model » bagging model (توسيع البحث), making model (توسيع البحث), ising model (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), wolf optimization (توسيع البحث)
aging model » bagging model (توسيع البحث), making model (توسيع البحث), ising model (توسيع البحث)
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1
ROC curves for the test set of four models.
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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2
SHAP bar plot.
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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3
Sample screening flowchart.
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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4
Descriptive statistics for variables.
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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5
SHAP summary plot.
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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6
Display of the web prediction interface.
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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7
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8
An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach
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. …"
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9
Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx
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. …"