Linked Life Data

11704441

Source:http://linkedlifedata.com/resource/pubmed/id/11704441

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2001-11-12
pubmed:abstractText
This paper presents a neural network (NN) model to evaluate an existing Health Risk Appraisal (HRA) for diabetes prediction over 3 years (1996-1998) based on a simulated learning algorithm on individual prognostic process, using the repeatedly measured HRAs of 6142 participants. The approach uses a sequential multi-layered perceptron (SMLP) with backpropagation learning, and an explicit model of time-varying inputs along with the sequentially obtained prediction probability, which was obtained by embedding a multivariate logistic function for consecutive years. The study captures the time-sensitive feature of associating risk factors as predictors to the occurrence of diabetes in the corresponding period. This approach outperforms the baseline classification and regression models in terms of gains (average profit: 0.18) and sensitivity (86.04%) for a test data. The result enables a time-sensitive disease prevention and management program as a prospective effort.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0933-3657
pubmed:author
pubmed:issnType
Print
pubmed:volume
23
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
277-93
pubmed:dateRevised
2009-11-3
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
A sequential neural network model for diabetes prediction.
pubmed:affiliation
The University of Michigan, 1027 E. Huron, Ann Arbor, MI 48104-1688, USA. kddum@umich.edu
pubmed:publicationType
Journal Article