Comparison of ranks for classification algorithms across performance metrics. حسب Stephen R. Piccolo (8367780)

Top 50 genes according to average rank across feature-selection algorithms for GSE10320 and GSE46691. حسب Stephen R. Piccolo (8367780)

Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE46691. حسب Stephen R. Piccolo (8367780)

Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE10320. حسب Stephen R. Piccolo (8367780)

Top-performing model configuration and performance by algorithm type. حسب Uğur Ejder (22683228)

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The flowchart of the proposed algorithm. حسب Muhammad Ayyaz Sheikh (18610943)

"…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"

Relative classification performance per combination of feature-selection and classification algorithm. حسب Stephen R. Piccolo (8367780)

Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection. حسب Stephen R. Piccolo (8367780)

The statistical description of the original data set of the patients (<i>n</i> = 162). حسب Uğur Ejder (22683228)

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The list of parameters of the modified data set for machine learning (<i>n</i> = 162). حسب Uğur Ejder (22683228)

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ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction. حسب Uğur Ejder (22683228)

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The accuracy score change matrix following model development. حسب Uğur Ejder (22683228)

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The comparison of the accuracy score of the benchmark and the proposed models. حسب Uğur Ejder (22683228)

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The prediction accuracy of simple versus hybrid models. حسب Uğur Ejder (22683228)

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Flow diagram of the proposed model. حسب Uğur Ejder (22683228)

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Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%). حسب Uğur Ejder (22683228)

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The most effective active substances for subfertility treatment. حسب Uğur Ejder (22683228)

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Confusion matrix of the ABC–LR–RF hybrid model for IVF outcome prediction. حسب Uğur Ejder (22683228)

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Flow diagram of the conversion of drugs into active substances. حسب Uğur Ejder (22683228)

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Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction. حسب Uğur Ejder (22683228)

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