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Hyung-Lae Kim 1 Article
Development of a Predictive Model for Type 2 Diabetes Mellitus Using Genetic and Clinical Data
Juyoung Lee, Bhumsuk Keam, Eun Jung Jang, Mi Sun Park, Ji Young Lee, Dan Bi Kim, Chang-Hoon Lee, Tak Kim, Bermseok Oh, Heon Jin Park, Kyu-Bum Kwack, Chaeshin Chu, Hyung-Lae Kim
Osong Public Health Res Perspect. 2011;2(2):75-82.   Published online June 30, 2011
DOI: https://doi.org/10.1016/j.phrp.2011.07.005
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  • 11 Crossref
AbstractAbstract PDFSupplementary Material
Objectives
Recent genetic association studies have provided convincing evidence that several novel loci and single nucleotide polymorphisms (SNPs) are associated with the risk of developing type 2 diabetes mellitus (T2DM). The aims of this study were: 1) to develop a predictive model of T2DM using genetic and clinical data; and 2) to compare misclassification rates of different models.
Methods
We selected 212 individuals with newly diagnosed T2DM and 472 controls aged in their 60s from the Korean Genome and Epidemiology Study. A total of 499 known SNPs from 87 T2DM-related genes were genotyped using germline DNA. SNPs were analyzed for significant association with T2DM using various classification algorithms including Quest (Quick, Unbiased, Efficient, Statistical tree), Support Vector Machine, C4.5, logistic regression, and K-nearest neighbor.
Results
We tested these models using the complete Korean Genome and Epidemiology Study cohort (n = 10,038) and computed the T2DM misclassification rates for each model. Average misclassification rates ranged at 28.2–52.7%. The misclassification rates for the logistic and machine-learning algorithms were lower than the statistical tree algorithms. Using 1-to-1 matched data, the misclassification rate of the statistical tree QUEST algorithm using body mass index and SNP variables was the lowest, but overall the logistic regression performed best.
Conclusions
The K-nearest neighbor method exhibited more robust results than other algorithms. For clinical and genetic data, our “multistage adjustment” model outperformed other models in yielding lower rates of misclassification. To improve the performance of these models, further studies using warranted, strategies to estimate better classifiers for the quantification of SNPs need to be developed.

Citations

Citations to this article as recorded by  
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    Computers in Biology and Medicine.2018; 103: 109.     CrossRef
  • Machine Learning and Data Mining Methods in Diabetes Research
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    Computational and Structural Biotechnology Journal.2017; 15: 104.     CrossRef
  • Survey on clinical prediction models for diabetes prediction
    N. Jayanthi, B. Vijaya Babu, N. Sambasiva Rao
    Journal of Big Data.2017;[Epub]     CrossRef
  • Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes
    Longfei Han, Senlin Luo, Jianmin Yu, Limin Pan, Songjing Chen
    IEEE Journal of Biomedical and Health Informatics.2015; 19(2): 728.     CrossRef
  • Depression among Korean Adults with Type 2 Diabetes Mellitus: Ansan-Community-Based Epidemiological Study
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    Osong Public Health and Research Perspectives.2015; 6(4): 224.     CrossRef

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