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

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.