Health Risk Assessment for Diabetes Mellitus Based on Longitudinal Analysis of MHTS Database
Journal of Medical Systems - Special issue: Vol I. on the international health evaluation conference, 1996
Neural Networks for Pattern Recognition
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Interference-less neural network training
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Modified Mixture of Experts for Diabetes Diagnosis
Journal of Medical Systems
An incremental EM-based learning approach for on-line prediction of hospital resource utilization
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An expert system based on analytical hierarchy process for diabetes risk assessment (DIABRA)
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Comparison of NN and LR classifiers in the context of screening native American elders with diabetes
Expert Systems with Applications: An International Journal
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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.