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results of the Hybrid Tree may improve over time
when the size of the dataset increases (Kohavi, 1996).
Due to the nature of the problem the results do not
present very high predicting power. Nevertheless,
comparing this study with results from the state of the
art, the obtained results are satisfactory.
In the future, several actions are planned. First, the
presented classifiers will be trained with larger
amount of data as new patients are included into the
study. In parallel, the ambulatory patient monitored
data will be studied to determine whether the
presented predictive models could be improved.
Next, we aim to build an integrated telemonitoring
system that integrates these predictive models to
support both clinicians, to manage best the patients,
and patients, to empower them in their disease
management and prevent potential decompensations.
Finally, this system will be tested in a trial study to
determine its usability.
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