Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes
Open Access
- 22 March 2010
- journal article
- research article
- Published by Springer Nature in BMC Medical Informatics and Decision Making
- Vol. 10 (1), 16
- https://doi.org/10.1186/1472-6947-10-16
Abstract
We present a potentially useful alternative approach based on support vector machine (SVM) techniques to classify persons with and without common diseases. We illustrate the method to detect persons with diabetes and pre-diabetes in a cross-sectional representative sample of the U.S. population. We used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES) to develop and validate SVM models for two classification schemes: Classification Scheme I (diagnosed or undiagnosed diabetes vs. pre-diabetes or no diabetes) and Classification Scheme II (undiagnosed diabetes or pre-diabetes vs. no diabetes). The SVM models were used to select sets of variables that would yield the best classification of individuals into these diabetes categories. For Classification Scheme I, the set of diabetes-related variables with the best classification performance included family history, age, race and ethnicity, weight, height, waist circumference, body mass index (BMI), and hypertension. For Classification Scheme II, two additional variables--sex and physical activity--were included. The discriminative abilities of the SVM models for Classification Schemes I and II, according to the area under the receiver operating characteristic (ROC) curve, were 83.5% and 73.2%, respectively. The web-based tool-Diabetes Classifier was developed to demonstrate a user-friendly application that allows for individual or group assessment with a configurable, user-defined threshold. Support vector machine modeling is a promising classification approach for detecting persons with common diseases such as diabetes and pre-diabetes in the population. This approach should be further explored in other complex diseases using common variables.Keywords
This publication has 19 references indexed in Scilit:
- A network view of disease and compound screeningNature Reviews Drug Discovery, 2009
- Improving the performance of physiologic hot flash measures with support vector machinesPsychophysiology, 2009
- Tools for Predicting the Risk of Type 2 Diabetes in Daily PracticeHormone and Metabolic Research, 2008
- How Effective Are Lifestyle Changes in the Prevention of Type 2 Diabetes Mellitus?Nutrition Reviews, 2008
- Diabetes Risk CalculatorDiabetes Care, 2008
- Standards of Medical Care in Diabetes—2008Diabetes Care, 2008
- De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measuresBioinformatics, 2007
- Global Guideline for Type 2 Diabetes: recommendations for standard, comprehensive, and minimal careDiabetic Medicine, 2006
- Mining protein function from text using term-based support vector machinesBMC Bioinformatics, 2005
- Reduction in the Incidence of Type 2 Diabetes with Lifestyle Intervention or MetforminNew England Journal of Medicine, 2002