Supplementary file 1_A multimodal MRI framework employing machine learning for detecting beginning cognitive impairment in Parkinson’s disease.docx ‌ Kevin Balßuweit (22687529)

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Table 1_A multimodal MRI framework employing machine learning for detecting beginning cognitive impairment in Parkinson’s disease.docx ‌ Kevin Balßuweit (22687529)

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Data Sheet 1_A multimodal MRI framework employing machine learning for detecting beginning cognitive impairment in Parkinson’s disease.pdf ‌ Kevin Balßuweit (22687529)

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

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

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

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

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

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

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

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

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

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

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

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

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

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