With the goal of supporting people su ering Type 1 Diabetes
Mellitus (T1DM), some mobile applications are being developed
based on arti cial intelligence techniques. Some of these applications are
based on Case-Based Reasoning methodologies (CBR) due to the advantage
regarding a personal, adapted recommendation. However, the
amount and quality of the cases in the CBR system will threat the system
outcome. Most of the maintenance methods developed deals with
classi cation tasks, while recommending an insulin dose (bolus) involves
a regression task. In this paper, a new maintenance method presented,
with the particularity of dealing with a regression tasks. The method is
applied over the Pepper insulin dose recommender system, and tested
using the UVA/Padova simulator, exhibiting the improvements of the
proposal in terms of both, the person health and the case-base size
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