Default and optimized parameters for each predictive model. by Emily S. Nichols (8740029)

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

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Personal algorithmic recommendations of potassium and phosphorous intake and the corresponding concentration predictions. by Jari Turkia (17912475)

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Nutrient predictors of the model. by Jari Turkia (17912475)

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

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Comparison of model structures for personal nutrient effects. by Jari Turkia (17912475)

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Data collection during the patients’ usual treatment routine. by Jari Turkia (17912475)

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Correlation plot of matrix C_<i>b</i> that includes the estimated within-model and cross-model correlations of personal effects. by Jari Turkia (17912475)

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DataSheet1_Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes.PDF by Eva Gonzalez-Flo (5046737)

Correlation plot of matrix C_<i>g</i> that includes the estimated within-model and cross-model correlations in level of dialysis treatment. by Jari Turkia (17912475)

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

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

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Posterior predictive check (PPC) of the final nutritional effect model. by Jari Turkia (17912475)

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Structure of the graphical model with general, treatment, and personal level nutrient effects. by Jari Turkia (17912475)

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

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QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm by Z.Y. Algamal (5547620)

“…Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”

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

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

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

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

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