Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes

By Weisen Yu, Tiebin Liu, R. Valdez, M. Gwinn, M. Khoury. 2010

459 citations recorded by the source corpus.

BackgroundWe 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.MethodsWe 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.ResultsFor 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.ConclusionsSupport 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.

Subfields: Health Information Management, Neurology

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Citations in the corpus

Federated learning of predictive models from federated Electronic Health Records (2018, methodology): “…supervised classification problems and solved using Support Vector Machines; the applications range from prediction of the onset of diabetes,[1,2] prediction of hospitalizations for cardiac events,[3] prediction of medication adherence in heart failure patients,[4] and cancer diagnosis,[5] to…”
Data mining in clinical big data: the frequently used databases, steps, and methodological models (2021, methodology): “[54] applied an SVM for predicting diabetes onset based on data from the National Health and Nutrition Examination Survey (NHANES).”
A data-driven approach to predicting diabetes and cardiovascular disease with machine learning (2019, methodology): “Based on the prior research [12, 13] in the domain, Logistic regression and SVM models were chosen as the performance baseline models for our study.”
Improving disease prediction using ICD-9 ontological features (2011, methodology): “[3] predicts diabetes risk using an SVM classifier.”