Smart city analytics: ensemble-learned prediction of citizen home care

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings

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.

Original languageEnglish
Title of host publicationProceedings of the 2017 ACM Conference on Information and Knowledge Management
Number of pages4
PublisherAssociation for Computing Machinery
Publication date2017
ISBN (Electronic)978-1-4503-4918-5
Publication statusPublished - 2017
Event26th ACM International Conference on Information and Knowledge Management - Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017
Conference number: 26


Conference26th ACM International Conference on Information and Knowledge Management

    Research areas

  • Ensemble learning, Home care, Smart city analytics

ID: 188363096