Search alternatives:
used optimization » based optimization (Expand Search), led optimization (Expand Search), guided optimization (Expand Search)
binary samples » biopsy samples (Expand Search), lunar samples (Expand Search)
dietary data » history data (Expand Search)
samples used » samples using (Expand Search)
used optimization » based optimization (Expand Search), led optimization (Expand Search), guided optimization (Expand Search)
binary samples » biopsy samples (Expand Search), lunar samples (Expand Search)
dietary data » history data (Expand Search)
samples used » samples using (Expand Search)
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The statistical description of the original data set of the patients (<i>n</i> = 162).
Published 2025Subjects: -
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The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
Published 2025Subjects: -
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ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction.
Published 2025Subjects: -
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The comparison of the accuracy score of the benchmark and the proposed models.
Published 2025Subjects: -
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Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).
Published 2025Subjects: -
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Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction.
Published 2025Subjects: -
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ROC curve for binary classification.
Published 2024“…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …”
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Confusion matrix for binary classification.
Published 2024“…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …”
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