Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles

TL;DR

This paper demonstrates that machine learning models can effectively detect early-stage diabetes by analyzing synthetic temporal glucose profiles, achieving over 85% accuracy in identifying insulin resistance.

Contribution

The study introduces a novel approach using ML on synthetic glucose time series data to diagnose early-stage diabetes, addressing data scarcity issues.

Findings

ML models achieved over 85% accuracy in detecting insulin resistance.

Synthetic glucose profiles effectively simulate real physiological patterns.

Multiple ML architectures performed comparably in diagnosis accuracy.

Abstract

Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are tightly regulated by two counter-regulatory hormones, insulin and glucagon, and the failure of the glucose homeostasis leads to the common metabolic disease, diabetes mellitus. It is a chronic disease that has a long latent period the complicates detection of the disease at an early stage. The vast majority of diabetics result from that diminished effectiveness of insulin action. The insulin resistance must modify the temporal profile of blood glucose. Thus we propose to use ML to detect the subtle change in the temporal pattern of glucose concentration. Time series data of blood glucose with sufficient resolution is currently unavailable, so we confirm the proposal using synthetic data of glucose profiles produced by a biophysical model that considers the glucose regulation and hormone action. Multi-layered perceptrons, convolutional neural networks, and recurrent neural networks all identified the degree of insulin resistance with high accuracy above $85\%$.