- 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
-
-
3,260
View
-
19
Download
-
11
Crossref
-
 Abstract
 PDF Supplementary 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  - Population stratification in type 2 diabetes mellitus: A systematic review
Sam Hodgson, Sukhmani Cheema, Zareena Rana, Doyinsola Olaniyan, Ellen O’Leary, Hermione Price, Hajira Dambha‐Miller
Diabetic Medicine.2022;[Epub] CrossRef - The Prediction of Diabetes
Lalit Kumar, Prashant Johri
International Journal of Reliable and Quality E-He.2022; 11(1): 1. CrossRef - Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine
Mohammad Farhan Khan, Gazal Kalyan, Sohom Chakrabarty, M. Mursaleen
Nutrients.2022; 14(14): 2794. CrossRef - Supervised and unsupervised algorithms for bioinformatics and data science
Ayesha Sohail, Fatima Arif
Progress in Biophysics and Molecular Biology.2020; 151: 14. CrossRef - Medical Internet of things using machine learning algorithms for lung cancer detection
Kanchan Pradhan, Priyanka Chawla
Journal of Management Analytics.2020; 7(4): 591. CrossRef - Perspective: Advancing Understanding of Population Nutrient–Health Relations via Metabolomics and Precision Phenotypes
Stephanie Andraos, Melissa Wake, Richard Saffery, David Burgner, Martin Kussmann, Justin O'Sullivan
Advances in Nutrition.2019; 10(6): 944. CrossRef - Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes
Dennis H. Murphree, Elaheh Arabmakki, Che Ngufor, Curtis B. Storlie, Rozalina G. McCoy
Computers in Biology and Medicine.2018; 103: 109. CrossRef - Machine Learning and Data Mining Methods in Diabetes Research
Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda
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
Chan Young Park, So Young Kim, Jong Won Gil, Min Hee Park, Jong-Hyock Park, Yeonjung Kim
Osong Public Health and Research Perspectives.2015; 6(4): 224. CrossRef
|
