  We present an ensemble learning method that predicts large increases in the
hours of home care received by citizens. The method is supervised, and uses
different ensembles of either linear (logistic regression) or non-linear
(random forests) classifiers. Experiments with data available from 2013 to 2017
for every citizen in Copenhagen receiving home care (27,775 citizens) show that
prediction can achieve state of the art performance as reported in similar
health related domains (AUC=0.715). We further find that competitive results
can be obtained by using limited information for training, which is very useful
when full records are not accessible or available. Smart city analytics does
not necessarily require full city records.
  To our knowledge this preliminary study is the first to predict large
increases in home care for smart city analytics.
