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Predicting Hyperglycemia Using NIR Spectrum of Spent Fluid in Hemodialysis Patients
Abstract
We aimed to assess the near infrared spectroscopy as a method for non-invasive on-line detection of hyperglycemia from spent hemodialysis effluent. We used partial least squares regression and several machine learning algorithms: random forest (RF), logistic regression, K-nearest neighbor (KNN), support vector machine (SVM), decision tree classifier, and Gaussian naive Bayes (NB) to classify normoglycemia from hyperglycemia. These classifier methods were used on the same dataset and evaluated by the area under the curve. The serum glucose levels were presented in the form of a binomial variable, where 0 indicated a glucose level within reference range and 1 a glucose level beyond the normal limit. For this reason, the methods of machine learning were applied as more specific methods of classification. RF and SVM have shown the best classification accuracy in predicting hyperglycemia, while decision tree and NB showed average accuracy.
Supporting agencies: This study was supported by the Ministry of Science and Education of Republic of Serbia (Project III45009 and III41006).
About the Authors
V. Matović
Belgrade University
Serbia
Serbia
11120 Belgrade.
J. Trbojević-Stanković
Belgrade University; Clinic of Urology, University Hospital Center “Dr Dragisa Misovic - Dedinje”
Serbia
Serbia
11120 Belgrade; 11000 Belgrade.
L. Matija
Belgrade University
Serbia
Serbia
11120 Belgrade.
D. Sarac
Belgrade University
Serbia
Serbia
11120 Belgrade.
A. Vasić-Milovanović
Belgrade University
Serbia
Serbia
11120 Belgrade.
A. Petrović
Belgrade University
Serbia
Serbia
11120 Belgrade.
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Review
For citations:
Matović V., Trbojević-Stanković J., Matija L., Sarac D., Vasić-Milovanović A., Petrović A. Predicting Hyperglycemia Using NIR Spectrum of Spent Fluid in Hemodialysis Patients. Zhurnal Prikladnoii Spektroskopii. 2021;88(3):504(1)-504(6).
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