A machine learning approach for type 2 diabetes diagnosis and prognosis using tailored heterogeneous feature subsets

• DOI: 10.1007/s11517-025-03355-5
• Publication Date: 2025-04-08T21:29:14Z
• Authors: J. Ramón Navarro-...; Pedro Pons-Suñer; Laura Arnal; Joaquim Arlandis; Rafael Llobet; Juan-Carlos Perez...; Francisco Lara-He...; Celeste Moya-Valera; Maria Elena Quiro...; Gemma Rojo-Martinez; Sergio Valdés; Eduard Montanya; Alfonso L. Calle-...; Josep Franch-Nadal; Elias Delgado; Luis Castaño; Ana-Bárbara Garcí...; Felipe Javier Chaves

Abstract Type 2 diabetes (T2D) is becoming one of the leading health problems in Western societies, diminishing quality of life and consuming a significant share of healthcare resources. This study presents machine learning models for T2D diagnosis and prognosis, developed using heterogeneous data from a Spanish population dataset ([e-mail] study). The models were trained exclusively on individuals classified as controls and undiagnosed diabetics, ensuring that the results are not influenced by treatment effects or behavioral changes due to disease awareness. Two data domains are considered: environmental (patient lifestyle questionnaires and measurements) and clinical (biochemical and anthropometric measurements). The preprocessing pipeline consists of four key steps: geospatial data extraction, feature engineering, missing data imputation, and quasi-constancy filtering. Two working scenarios (Environmental and Healthcare) are defined based on the features used, and applied to two targets (diagnosis and prognosis), resulting in four distinct models. The feature subsets that best predict the target have been identified based on permutation importance and sequential backward selection, reducing the number of features and, consequently, the cost of predictions. In the Environmental scenario, models achieved an AUROC of 0.86 for diagnosis and 0.82 for prognosis. The Healthcare scenario performed better, with an AUROC of 0.96 for diagnosis and 0.88 for prognosis. A partial dependence analysis of the most relevant features is also presented. An online demo page showcasing the Environmental and Healthcare T2D prognosis models is available upon request.

Graphic abstract