Estimation and svm classification of glucose-insulin model parameters from OGTT data. An aid for diabetes diagnostics

TL;DR

This paper proposes a method to estimate parameters of glucose-insulin models from OGTT data and uses SVM classification to assist in diagnosing diabetes and glucose intolerance.

Contribution

It introduces a new parameter estimation approach for glucose-insulin models and applies SVM classification to improve diabetes diagnostics.

Findings

Parameter estimation from OGTT data is feasible.

SVM classification effectively distinguishes between diabetic and non-diabetic cases.

The method aids in early and accurate diabetes diagnosis.

Abstract

In the Oral Glucose Tolerance Test (OGTT), a patient, after an overnight fast ingests a load of glucose. Then measurements of glucose concentration are taken every 30 minutes during two hours. The test is used to aid diagnosis of diabetes, namely, type 2 diabetes mellitus and glucose intolerance. Several mathematical models have been introduced to describe the glucose-insulin system during an OGTT. Models consist on systems of differential equations where most parameters are unknown. Estimation of these parameters is an aim of this work. In a minimal model, two of such parameters are proposed for classification by means of a SVM technique. Consequently, a case is made for this classification as an aid for diagnosis.