LightGBM: an efficient and accurate method for predicting pregnancy diseases

References

• Akay MF. 2009. Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications 36:3240–3247.
   Web of Science ®Google Scholar
• Akbulut A, Ertugrul E, Topcu V. 2018. Fetal health status prediction based on maternal clinical history using machine learning techniques. Computer Methods and Programs in Biomedicine 163:87–100.
   PubMed Web of Science ®Google Scholar
• Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, Mcclelland R, et al. 2017. Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circulation Research 121:1092–1101.
   PubMed Web of Science ®Google Scholar
• Breiman L. 2001. Random forests. Machine Learning 45:5–32.
   Web of Science ®Google Scholar
• Brown MA, Lindheimer MD, De Swiet M, Assche AV, Moutquin J-M. 2001. The classification and diagnosis of the hypertensive disorders of pregnancy: statement from the International Society for the Study of Hypertension in Pregnancy (ISSHP). Hypertension in Pregnancy 20:ix–xiv.
   PubMed Web of Science ®Google Scholar
• Chen T, Xu J, Ying H, Chen X, Feng R, Fang X, et al. 2019. Prediction of extubation failure for intensive care unit patients using light gradient boosting machine. IEEE Access. 7:150960–150968.
   Web of Science ®Google Scholar
• Clark TG, Altman DG. 2003. Developing a prognostic model in the presence of missing data: an ovarian cancer case study. Journal of Clinical Epidemiology 56:28–37.
   PubMed Web of Science ®Google Scholar
• Cortes C, Vapnik V. 1995. Support-vector networks. Machine Learning 20:273–297.
   Web of Science ®Google Scholar
• Deo RC. 2015. Machine learning in medicine. Circulation 132:1920–1930.
   PubMed Web of Science ®Google Scholar
• Fan Y, Li Y, Li Y, Feng S, Bao X, Feng M, Wang R. 2020. Development and assessment of machine learning algorithms for predicting remission after transsphenoidal surgery among patients with acromegaly. Endocrine 67:412–422.
   PubMed Web of Science ®Google Scholar
• Freund Y, Schapire RE. 1996. Experiments with a new boosting algorithm. Machine Learning. Proceedings of the Thirteenth International Conference (ICML '96), Bari, Italy. p. 148–156.
   Google Scholar
• Friedman JH. 2001. Greedy function approximation A gradient boosting machine. The Annals of Statistics 29:1189–1232.
   Web of Science ®Google Scholar
• Goodman KE, Lessler J, Cosgrove SE, Harris AD, Lautenbach E, Han JH, Milstone AM, Massey CJ, Tamma PD, Antibacterial Resistance Leadership Group. 2016. A clinical decision tree to predict whether a bacteremic patient is infected with an extended-spectrum β-lactamase-producing organism. Clinical Infectious Diseases 63:896–903.
   PubMed Web of Science ®Google Scholar
• Greenland S, Finkle WD. 1995. A critical look at methods for handling missing covariates in epidemiologic regression analyses. American Journal of Epidemiology 142:1255–1265.
   PubMed Web of Science ®Google Scholar
• Jayasurya K, Fung G, Yu S, Dehing-Oberije C, De Ruysscher D, Hope A, et al. 2010. Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. Medical Physics 37:1401–1407.
   PubMed Web of Science ®Google Scholar
• Kate RJ, Perez RM, Mazumdar D, Pasupathy KS, Nilakantan V. 2016. Prediction and detection models for acute kidney injury in hospitalized older adults. BMC Medical Informatics and Decision Making 16:39.
   PubMed Web of Science ®Google Scholar
• Ke GL, Meng Q, Finley T, Wang TF, Chen W, Ma WD, et al. 2017. LightGBM: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30:30.
   Google Scholar
• Kwon JM, Lee Y, Lee Y, Lee S, Park J. 2018. An algorithm based on deep learning for predicting in-hospital cardiac arrest. Journal of the American Heart Association 7:e008678.
   Web of Science ®Google Scholar
• Lammert F, Marschall H-U, Glantz A, Matern S. 2000. Intrahepatic cholestasis of pregnancy molecular pathogenesis diagnosis and management. Journal of Hepatology 33:1012–1021.
   PubMed Web of Science ®Google Scholar
• Lee BJ, Kim JY. 2016. Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE Journal of Biomedical and Health Informatics 20:39–46.
   PubMed Web of Science ®Google Scholar
• Morikawa M, Yamada T, Yamada T, Cho K, Sato S, Minakami H. 2014. Seasonal variation in the prevalence of pregnancy-induced hypertension in Japanese women. The Journal of Obstetrics and Gynaecology Research 40:926–931.
   PubMed Web of Science ®Google Scholar
• Poon LC, Kametas NA, Maiz N, Akolekar R, Nicolaides KH. 2009. First-trimester prediction of hypertensive disorders in pregnancy. Hypertension 53:812–818.
   PubMed Web of Science ®Google Scholar
• Quinlan JR. 1986. Induction of decision trees. Machine Learning 1:81–106.
   Google Scholar
• Quinlan, J. R. 1996. Bagging, boosting, and C4.5. Proceedings of the Thirteenth National Conference on Artificial Intelligence and the Eighth Innovative Applications of Artificial Intelligence Conference, Vols. 1 and 2, Portland, OR. p. 725–730.
   Google Scholar
• Schnack HG, Kahn RS. 2016. Detecting neuroimaging biomarkers for psychiatric disorders: sample size matters. Frontiers in Psychiatry 7:50.
   PubMed Web of Science ®Google Scholar
• Sehgal, M. S. B., Gondal, I. & Dooley, L. 2004. K-ranked covariance based missing values estimation for microarray data classification. Fourth International Conference on Hybrid Intelligent Systems, Kitakyushu, Japan. p. 274–279.
   Google Scholar
• Sims CJ, Meyn L, Caruana R, Rao RB, Mitchell T, Krohn M. 2000. Predicting cesarean delivery with decision tree models. American Journal of Obstetrics and Gynecology 183:1198–1206.
   PubMed Web of Science ®Google Scholar
• Tan C, Chen H, Xia C. 2009. Early prediction of lung cancer based on the combination of trace element analysis in urine and an Adaboost algorithm. Journal of Pharmaceutical and Biomedical Analysis. 49:746–752.
   PubMed Web of Science ®Google Scholar
• Zacharaki EI, Kanas VG, Davatzikos C. 2011. Investigating machine learning techniques for MRI-based classification of brain neoplasms. International Journal of Computer Assisted Radiology and Surgery 6:821–828.
   PubMed Web of Science ®Google Scholar