Top-performing model configuration and performance by algorithm type. 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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The statistical description of the original data set of the patients (<i>n</i> = 162). 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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The list of parameters of the modified data set for machine learning (<i>n</i> = 162). 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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Supplementary Material 8 حسب Nishitha R Kumar (19750617)

"…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…"