Predictive models to improve the wellbeing of heart-failure patients
The paper presents an approach to providing advice on health related quality of life to patients with congestive heart failure, using predictive models built from telemonitoring data. First, by combining machine learning algorithms, feature construction, feature selection and expert knowledge, we built a set of predictive models. We then identified which of the features present in the models can be changed by the patients themselves with an appropriate intervention and modelled the association between them and all the other features using linear models.