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11041 |
Prediction of contraceptive discontinuation among reproductive-age women in Ethiopia using Ethiopian Demographic and Health Survey 2016 Dataset: A Machine Learning Approach
Enthalten in BMC medical informatics and decision making Bd. 23, 17.1.2023, Nr. 1, date:12.2023: 1-17
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11042 |
Prediction of contrast-associated acute kidney injury with machine-learning in patients undergoing contrast-enhanced computed tomography in emergency department
Enthalten in Scientific reports Bd. 15, 27.2.2025, Nr. 1, date:12.2025: 1-12
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11043 |
Prediction of coronary artery lesions in children with Kawasaki syndrome based on machine learning
Enthalten in BMC pediatrics Bd. 24, 5.3.2024, Nr. 1, date:12.2024: 1-9
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11044 |
Prediction of COVID-19 patients’ participation in financing informal care using machine learning methods: willingness to pay and willingness to accept approaches
Enthalten in BMC health services research Bd. 24, 10.7.2024, Nr. 1, date:12.2024: 1-13
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11045 |
PREDICTION OF DEFORMATION CAUSED BY LANDSLIDES BASED ON GRAPH CONVOLUTION NETWORKS ALGORITHM AND DINSAR TECHNIQUE
Enthalten in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. X-4/W1-2022, 2023: 391-397. 7 S.
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11046 |
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
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11047 |
Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques
Enthalten in Scientific reports Bd. 11, 13.1.2021, Nr. 1, date:12.2021: 1-9
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11048 |
Prediction of depressive disorder using machine learning approaches: findings from the NHANES
Enthalten in BMC medical informatics and decision making Bd. 25, 17.2.2025, Nr. 1, date:12.2025: 1-12
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11049 |
Prediction of diabetes disease using an ensemble of machine learning multi-classifier models
Enthalten in BMC bioinformatics Bd. 24, 12.9.2023, Nr. 1, date:12.2023: 1-24
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11050 |
Prediction of disease progression and outcomes in multiple sclerosis with machine learning
Enthalten in Scientific reports Bd. 10, 3.12.2020, Nr. 1, date:12.2020: 1-13
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