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

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,

SUBMITTER: Navarro-Cerdan JR 

PROVIDER: S-EPMC12402034 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature