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Journal Articles Engineering Applications of Artificial Intelligence Year : 2023

Hypoglycaemia prediction using information fusion and classifiers consensus

Abstract

The recommendation that there must be a balance between insulin, food, and exercise to keep diabetes under control provides an opportunity for developing mobile applications for self-management of the disease. Real predictions can improve the quality of patients’ lives by avoiding unwanted events, namely, hypoglycaemia. We proposed a hypoglycaemia prediction approach combining information fusion and classifiers consensus to predict the risk of hypoglycaemia in a 24-h window. First, we train a multi-classifiers system from different sources of different patients. After using data from a unique patient, we performed the prediction of the risk of hypoglycaemia and evaluate the consensus decision of the single models resulting from the learning process. The predictions were performed for 54 patients from the University of California Irvine diabetes dataset. The results from classifiers consensus decision provide very promising results, which are acceptable considering that we used sparse data and data from self-monitoring blood glucose. Our approach shows that with a 24-h window is possible to catch appropriate patterns associated with the risk of hypoglycaemia and proposed a solution that can improve the hypoglycaemia prediction with a higher specificity, i.e. less false alarms, when compared with similar literature.

Dates and versions

hal-04321113 , version 1 (04-12-2023)

Identifiers

Cite

Virginie Felizardo, Nuno Garcia, Imen Megdiche, Nuno Pombo, Miguel Sousa, et al.. Hypoglycaemia prediction using information fusion and classifiers consensus. Engineering Applications of Artificial Intelligence, 2023, 123 (part A), pp.106194. ⟨10.1016/j.engappai.2023.106194⟩. ⟨hal-04321113⟩
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