Search alternatives:
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
dietary data » history data (Expand Search)
data feature » data figure (Expand Search), each feature (Expand Search), a feature (Expand Search)
binary task » binary mask (Expand Search)
task driven » task derived (Expand Search), mapk driven (Expand Search), state driven (Expand Search)
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
dietary data » history data (Expand Search)
data feature » data figure (Expand Search), each feature (Expand Search), a feature (Expand Search)
binary task » binary mask (Expand Search)
task driven » task derived (Expand Search), mapk driven (Expand Search), state driven (Expand Search)
-
1
-
2
The statistical description of the original data set of the patients (<i>n</i> = 162).
Published 2025Subjects: -
3
The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
Published 2025Subjects: -
4
ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction.
Published 2025Subjects: -
5
-
6
The comparison of the accuracy score of the benchmark and the proposed models.
Published 2025Subjects: -
7
-
8
-
9
Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).
Published 2025Subjects: -
10
-
11
-
12
-
13
Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction.
Published 2025Subjects: -
14
Data Sheet 5_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf
Published 2025“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”
-
15
Data Sheet 7_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf
Published 2025“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”
-
16
Data Sheet 6_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf
Published 2025“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”
-
17
Data Sheet 3_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf
Published 2025“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”
-
18
Data Sheet 1_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf
Published 2025“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”
-
19
Data Sheet 2_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf
Published 2025“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”
-
20
Data Sheet 4_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf
Published 2025“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”