Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study
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K. S. Joseph | K. Joseph | A. Allen | V. Allen | S. Kuhle | Stefan Kuhle | B. Maguire | Hongqun Zhang | David C. Hamilton | Bryan Maguire | Hongqun Zhang | David Hamilton | Alexander C. Allen | Victoria M. Allen
[1] V. Han,et al. Determinants of small for gestational age birth at term. , 2012, Paediatric and perinatal epidemiology.
[2] K. Joseph,et al. Validation of perinatal data in the Discharge Abstract Database of the Canadian Institute for Health Information. , 2009, Chronic diseases in Canada.
[3] E. Gratacós,et al. Differential performance of first‐trimester screening in predicting small‐for‐gestational‐age neonate or fetal growth restriction , 2017, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.
[4] K. Maršál,et al. Predicting risk for large‐for‐gestational age neonates at term: a population‐based Bayesian theorem study , 2013, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.
[5] Hong Qiao,et al. Comparing data mining methods with logistic regression in childhood obesity prediction , 2009, Inf. Syst. Frontiers.
[6] K. Bergmann,et al. Secular trends in neonatal macrosomia in Berlin: influences of potential determinants. , 2003, Paediatric and perinatal epidemiology.
[7] R W Platt,et al. A new and improved population-based Canadian reference for birth weight for gestational age. , 2001, Pediatrics.
[8] A. Souka,et al. First trimester prediction of small‐ and large‐for‐gestation neonates by an integrated model incorporating ultrasound parameters, biochemical indices and maternal characteristics , 2012, Acta obstetricia et gynecologica Scandinavica.
[9] F. Mcauliffe,et al. Prediction and prevention of the macrosomic fetus. , 2012, European journal of obstetrics, gynecology, and reproductive biology.
[10] G. Bonsel,et al. An antenatal prediction model for adverse birth outcomes in an urban population: The contribution of medical and non-medical risks. , 2016, Midwifery.
[11] William N. Venables,et al. Modern Applied Statistics with S , 2010 .
[12] E. Goto. Ultrasound as a primary screening tool for detecting low birthweight newborns , 2016, Medicine.
[13] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[14] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[15] E. M. Breed,et al. Risk factors and obstetric complications associated with macrosomia , 2004, International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics.
[16] S. Lederman,et al. Maternal Reporting of Prepregnancy Weight and Birth Outcome: Consistency and Completeness Compared with the Clinical Record , 1998, Maternal and Child Health Journal.
[17] Ann L. Yaktine,et al. Weight Gain During Pregnancy , 2009 .
[18] G. Sysyn,et al. Abnormal fetal growth: intrauterine growth retardation, small for gestational age, large for gestational age. , 2004, Pediatric clinics of North America.
[19] Youth,et al. Weight Gain During Pregnancy: Reexamining the Guidelines , 2010 .
[20] W. Callaghan,et al. Health Care Utilization in the First Year of Life among Small- and Large- for-Gestational Age Term Infants , 2013, Maternal and Child Health Journal.
[21] Ian R White,et al. Screening for fetal growth restriction with universal third trimester ultrasonography in nulliparous women in the Pregnancy Outcome Prediction (POP) study: a prospective cohort study , 2015, The Lancet.
[22] M C McCormick,et al. The contribution of low birth weight to infant mortality and childhood morbidity. , 1985, The New England journal of medicine.
[23] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[24] K. Nicolaides,et al. First-Trimester Prediction of Macrosomia , 2010, Fetal Diagnosis and Therapy.
[25] A. Baschat,et al. First-trimester prediction of small-for-gestational age neonates incorporating fetal Doppler parameters and maternal characteristics. , 2014, American journal of obstetrics and gynecology.
[26] C. Levitt. Canadian Perinatal Surveillance System. , 1998, Canadian family physician Medecin de famille canadien.
[27] Max Kuhn,et al. Applied Predictive Modeling , 2013 .
[28] Mario Castro,et al. Predicting Intermediate Phenotypes in Asthma Using Bronchoalveolar Lavage‐Derived Cytokines , 2010, Clinical and translational science.
[29] R. Romero,et al. Single and Serial Fetal Biometry to Detect Preterm and Term Small- and Large-for-Gestational-Age Neonates: A Longitudinal Cohort Study , 2016, PloS one.
[30] K. Nicolaides,et al. Ultrasonographic estimation of fetal weight: development of new model and assessment of performance of previous models , 2018, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.
[31] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[32] W. Plasencia,et al. First‐trimester screening for large‐for‐gestational‐age infants , 2012, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.
[33] Jun Zhang,et al. Predicting large fetuses at birth: do multiple ultrasound examinations and longitudinal statistical modelling improve prediction? , 2012, Paediatric and perinatal epidemiology.
[34] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[35] Lucila Ohno-Machado,et al. Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.
[36] Lily S Lee,et al. Risk factors for preterm birth and small-for-gestational-age births among Canadian women. , 2013, Paediatric and perinatal epidemiology.