Work

Predictive model for Type 2 Diabetes Risk using Artificial Intelligence and multivariate data

Year: 2025

Type: article

Abstract: Type 2 Diabetes Mellitus (T2DM) is a global public health concern due to its growing prevalence and silent progression. This study presents the development and validation of a predictive model based on artificial neural networks (ANN) using multivariate, non-invasive data. The model integrates three data sources: near-infrared spectroscopy (NIRS) for estimated glucose levels, plantar pressure sensors (FSR) for neuropathy indicators, and a digital lifestyle questionnaire (IMEVID). After preprocessing and training using a stratified dataset of adults without previous diagnosis, the ANN achieved 82 % accuracy, with high sensitivity and a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of 0.89. Compared to traditional methods like logistic regression, the proposed model demonstrated superior performance in classifying individuals at high or low risk. These results demonstrate the potential of non-invasive, AI-based screening tools to identify early-stage metabolic dysfunction, enabling timely preventive interventions. This approach may be especially useful in community-based or resource-limited settings to support public health decision-making.

Source: Revista de Aplicaciones de la Ingeniería

DOI: 10.35429/jea.2025.12.33.2.1.7

Authors: Jesús Rolando Rodríguez-Silva, Nancy Griselda Pérez-Briones, Janeth Margarita Ventura-Sobrevilla, Victor Daniel Boone-Villa

Institution: Universidad Autónoma de Coahuila

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Consejo Estatal de Ciencia y Tecnología de Jalisco, Universidad Autónoma de Coahuila, Fondo para la Investigación Científica y Tecnológica