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171 - 180 von 193

	171	Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods Enthalten in BMC medical informatics and decision making Bd. 15, 14.10.2015, Nr. 1, date:12.2015: 1-10
	172	Prediction of irrigation water quality indices based on machine learning and regression models Enthalten in Applied water science Bd. 12, 21.3.2022, Nr. 4, date:4.2022: 1-14
	173	Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches Enthalten in Scientific reports Bd. 14, 20.9.2024, Nr. 1, date:12.2024: 1-9
	174	Predictors of High Healthcare Cost Among Patients with Generalized Myasthenia Gravis: A Combined Machine Learning and Regression Approach from a US Payer Perspective Enthalten in Applied health economics and health policy Bd. 22, 13.7.2024, Nr. 5, date:9.2024: 735-747
	175	Quantifying the effects of neutron dose, dose protraction, age and sex on mouse survival using parametric regression and machine learning on a 21,000-mouse data set Enthalten in Scientific reports Bd. 13, 9.12.2023, Nr. 1, date:12.2023: 1-13
	176	Regression prediction of tobacco chemical components during curing based on color quantification and machine learning Enthalten in Scientific reports Bd. 14, 7.11.2024, Nr. 1, date:12.2024: 1-16
	177	Revealing accuracy in climate dynamics: enhancing evapotranspiration estimation using advanced quantile regression and machine learning models Enthalten in Applied water science Bd. 14, 24.6.2024, Nr. 7, date:7.2024: 1-22
	178	Revised Empirical Relations Between Earthquake Source and Rupture Parameters by Regression and Machine Learning Algorithms Enthalten in Pure and applied geophysics Bd. 180, 5.9.2023, Nr. 10, date:10.2023: 3477-3494
	179	Robust Multi-Output Machine Learning Regression for Seismic Hazard Model Using Peak Crust Acceleration Case Study, Turkey, Iraq and Iran Enthalten in Journal of earth science Bd. 34, 18.10.2023, Nr. 5, date:10.2023: 1447-1464
	180	SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data Enthalten in Genome biology Bd. 25, 14.10.2024, Nr. 1, date:12.2024: 1-28

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