OBJECTIVES Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients’ demographic characteristics and clinical features.
METHODS
In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used.
RESULTS
For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy.
CONCLUSIONS
The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases.
Summary
Citations
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OBJECTIVES Diabetes is a major public health problem that is approaching epidemic proportions globally. Diabetes self-management can reduce complications and mortality in type 2 diabetic patients. The purpose of this study was to examine associations between diabetes self-management and microvascular complications in patients with type 2 diabetes.
METHODS
In this cross-sectional study, 562 Iranian patients older than 30 years of age with type 2 diabetes who received treatment at the Diabetes Research Center of the Endocrinology and Metabolism Research Institute of the Tehran University of Medical Sciences were identified. The participants were enrolled and completed questionnaires between January and April 2014. Patients’ diabetes self-management was assessed as an independent variable by using the Diabetes Self-Management Questionnaire translated into Persian. The outcomes were the microvascular complications of diabetes (retinopathy, nephropathy, and neuropathy), identified from the clinical records of each patient. A multiple logistic regression model was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) between diabetes self-management and the microvascular complications of type 2 diabetes, adjusting for potential confounders.
RESULTS
After adjusting for potential confounders, a significant association was found between the diabetes self-management sum scale and neuropathy (adjusted OR, 0.64; 95% CI, 0.45 to 0.92, p=0.01). Additionally, weak evidence was found of an association between the sum scale score of diabetes self-management and nephropathy (adjusted OR, 0.71; 95% CI, 0.47 to 1.05, p=0.09).
CONCLUSIONS
Among patients with type 2 diabetes, a lower diabetes self-management score was associated with higher rates of nephropathy and neuropathy.
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