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...

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Bibliographic Details
Published in:Chinese medical journal 2012-03, Vol.125 (5), p.851-857
Main Authors: Li, Chang-ping, Zhi, Xin-yue, Ma, Jun, Cui, Zhuang, Zhu, Zi-long, Zhang, Cui, Hu, Liang-ping
Format: Article
Language:English
Subjects:
2型糖尿病
Case-Control Studies
Decision Trees
Diabetes Mellitus, Type 2 - complications
Diabetic Neuropathies - diagnosis
Diabetic Neuropathies - etiology
Humans
Logistic Models
Logistic回归
决策树
周围神经
多层感知器
性能比较
病变
预测模型
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Description
Summary: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 build accuracy models quickly and efficiently, and how to assess the generalization ability （that is, prediction performance） reliably by Monte Carlo method in the case of small sample size.
ISSN:0366-6999
2542-5641
DOI:10.3760/cma.j.issn.0366-6999.2012.05.022