Work

Use of the Geometric Pattern Transformation to detect diabetes mellitus type 2 from blood glucose signals

Year: 2025

Type: article

Abstract: Type 2 Diabetes Mellitus (DMT2) is a disease with a high incidence worldwide, and various estimates project an increase in the near future. This research introduces a new methodology based on blood glucose concentration recordings obtained from Continuous Glucose Monitors (CGM) for the detection of the disease. The main research question is whether it is possible to enhance the detection ability of DMT2 by applying Geometric Pattern Transformation (GPT) to the glucose records, compared to basic statistical tools. The standard deviation of the glucose signal from continuous glucose monitoring (CGM) was evaluated as a parameter to distinguish between individuals with and without diabetes. The GPT technique was then applied to the glucose data to assess its effectiveness in enhancing disease detection compared to traditional statistical methods. The findings indicate that traditional statistical tools are insufficient to achieve the same performance as the proposed method for detecting DMT2. Applying GPT significantly improved detection accuracy, showing a clear advantage in differentiating between subjects with and without DMT2. Moreover, glucose monitoring over a period of at least 3 days proved to be as effective as longer periods for detection purposes. The proposed methodology, using basic mathematical operations on glucose data, effectively distinguishes individuals with DMT2 from those without. The parameters used to assess detection quality demonstrate a marked improvement due to GPT. Additionally, a 3-day monitoring period is sufficient for reliable detection, potentially streamlining the diagnostic process.

Source: Biomedical Engineering Advances

DOI: 10.1016/j.bea.2025.100205

Authors: Marcos Maillot, Ariel Amadio, Cristian Bonini, Leandro Robles Dávila, Walter Legnani, Dino Otero

Institution: National Technological University

Language: English

Topic: Diabetes Management and Research

Subfield: Endocrinology, Diabetes and Metabolism

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,020