Impact of the glycaemic sampling method in diabetes data mining

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Machado, D (Autor) Outra; Costa, VS (Autor) FCUP; Pedro Brandão (Autor) FCUP

Ata de Conferência Internacional

Título: IEEE Symposium on Computers and Communications, ISCC 2022, Rhodes, Greece, June 30 - July 3, 2022

Páginas: 1-6

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00X-BHV

DOI: 10.1109/iscc55528.2022.9912822

Abstract (EN): Finger-pricking is the traditional procedure for glycaemia monitoring. It is an invasive method where the person with diabetes is required to prick their finger. In recent years, continuous-glucose monitoring (CGM), a new and more convenient method of glycaemia monitoring, has become prevalent. CGM provides continuous access to glycaemic values without the need of finger-pricking. Data mining can be used to understand glycaemic values, and to ideally warn users of abnormal situations. CGM provides significantly more data than finger-pricking. Thus, the amount and value of CGM data ultimately questions the role of finger-pricking for glycaemic studies. In this work we use the OhioT1DM data set in order to study the importance of finger-prick-based data. We use Random Forest as a classification method, a robust method that tends to obtain quality results. Our results indicate that, although more demanding and scarcer, finger-prick-based glycaemic values have a significant role on diabetes management and on data mining.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6