Performance comparison between Logistic regression, decision trees, and multilayer perceptron in predicting peripheral neuropathy in type 2 diabetes mellitusOACSCDCSTPCD
Performance comparison between Logistic regression, decision trees, and multilayer perceptron in predicting peripheral neuropathy in type 2 diabetes mellitus
Background Various methods can be applied to build predictive models for the clinical data with binary outcome variable.This research aims to explore the process of constructing common predictive models,Logistic regression (LR),decision tree (DT) and multilayer perceptron (MLP),as well as focus on specific details when applying the methods mentioned above:what preconditions should be satisfied,how to set parameters of the model,how to screen variables and bu…查看全部>>
LI Chang-ping;ZHI Xin-yue;MA Jun;CUI Zhuang;ZHU Zi-long;ZHANG Cui;HU Liang-ping
Department of Health Statistics, College of Public Health,Tianjin Medical University, Tianjin 300070, ChinaDepartment of Epidemiology, College of Public Health,Tianjin Medical University, Tianjin 300070, ChinaDepartment of Health Statistics, College of Public Health,Tianjin Medical University, Tianjin 300070, ChinaDepartment of Health Statistics, College of Public Health,Tianjin Medical University, Tianjin 300070, ChinaDepartment of Internal Neurology, Tianjin Huanhu Hospital,Tianjin 300060, ChinaKey Laboratory of Advanced Energy Materials Chemistry(Ministry of Education), Nankai University, Tianjin 300071, ChinaConsulting Center of Biomedical Statistics, Academy of Military Medical Science, Beiiing 100850, China
Logistic regressiondecision treemultilayer perceptrondiabetic peripheral neuropathy
Logistic regressiondecision treemultilayer perceptrondiabetic peripheral neuropathy
《中华医学杂志（英文版）》 2012 (5)
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This work was supported by the grants from National Natural Science Foundation of China (No.21003077),College of Public Health of Tianjin Medical University in China (No.GWKY-2010-01),the Open Project of Key Laboratory of Advanced Energy Materials Chemistry (No. KLAEMCOP201101) and Natural Science Foundation of Tianjin China (No.08JCZDJC21400).
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