Shown is the custom train/test split methodology.

<p>a. Each day/night is given a unique identifier. b. The dataset is divided into “day” and “night” datasets. Days/nights where one or more hypoglycemic events were experienced by the participant are identified (highlighted in grey). c. Considering the night dataset to illustrate our approach,...

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Bibliographic Details
Main Author:	Yasmine M. Mohamed (21601663) (author)
Other Authors:	José Mancera (5329439) (author), Andreas Brandenberg (21601666) (author), Stefan Fischli (735662) (author), Michael M. Havranek (4620655) (author)
Published:	2025
Subjects:	Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified participant &# 8217 occur despite advancements based ensemble algorithm personalized manner using analyzed using shap model &# 8217 9 %, 28 xlink "> hypoglycemia noninvasive hypoglycemia detection cardiac features demonstrated type 1 diabetes seventeen personalized models guiding future research 2 %) followed sleeping times </ sleeping times model decisions 5 %, 5 %) 2 %). respiratory features input features hypoglycemia prediction built using smartwatches remain results offer relative importance medical devices major challenge machine learning important aspect highest importance growing body grade wearable glycemic monitoring finger pricking feature importance diabetes management current methods average specificity average area
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Shown is the custom train/test split methodology.

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