Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross-sectional study

Abstract

OBJECTIVESDiabetes 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.METHODSIn 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.RESULTSFor 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.CONCLUSIONSThe 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.

Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data

“…It was not our goal for this model to be used for individual level patient care. Our model was trained on data from over 1.5 million patients from Ontario, which is among one of the most diverse populations in the world and, to our knowledge, one of the largest prediction modelling studies that takes into account multiple types of diabetes complications [22][23][24][25][26][27][28][29][30][31][32][33][34][51][52][53] . Our model was also wellcalibrated and showed good discrimination.…”

“…Many prognostic models have been developed for diabetes complications in the clinical setting [22][23][24] , including more recent applications of machine learning approaches [25][26][27][28][29][30][31][32][33][34] . These models generally have made use of rich suites of features (e.g., body mass index, smoking status, biomarkers ranging from commonly ordered lipids to extensive genetic panels) extracted from electronic medical records (EMRs) 25,27,[31][32][33] or clinical trials 28,30 .…”

Diabetic Sensorimotor Polyneuropathy Severity Classification Using Adaptive Neuro Fuzzy Inference System

“…Therefore, this research aims to propose a DSPN severity classifier using the ANFIS algorithm to enhance the diagnosis facility for DSPN patients by early, accurate and reproducible screening and stratification of DSPN. In the literature, the Fuzzy Inference System (FIS) and support vector machine (SVM) algorithms have been introduced for detecting and classifying the severity of DSPN [20][21][22][23]. DSPN onset is insidious and progresses differently for every patient which makes it exhibits non-linear characteristics.…”

“…Researchers using the fuzzy system for classification of the severity of DSPN have to design non-linear characteristics of DSPN through the IF-THEN rules which leave a chance of human error and reliant on expert knowledge, thus its accuracy is questionable. On the other hand, Kazemi et al [23] have identified different levels of DPN severity by using multicategory SVM (MSVM) and considered the neuropathy disability score (NDS) as the input of their MSVM classifier. However, the accuracy of their system was only 76%.…”

“…As the fuzzy system works based on if-then rules, there is always a chance to have a human error in defining the rules for identifying DSPN, thus the accuracy of the classification is questionable. One study has been conducted to identify the different levels of DSPN severity by Kazemi et al [23] using a multicategory support vector machine with NDS as the input of their model. However, the model performance accuracy was only 76%.…”