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

TY - GEN

T1 - Smart city analytics

T2 - 26th ACM International Conference on Information and Knowledge Management

AU - Hansen, Casper

AU - Hansen, Christian

AU - Alstrup, Stephen

AU - Lioma, Christina

N1 - Conference code: 26

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Ensemble learning

KW - Home care

KW - Smart city analytics

UR - http://www.scopus.com/inward/record.url?scp=85037344580&partnerID=8YFLogxK

U2 - 10.1145/3132847.3133101

DO - 10.1145/3132847.3133101

M3 - Article in proceedings

AN - SCOPUS:85037344580

SP - 2095

EP - 2098

BT - Proceedings of the 2017 ACM Conference on Information and Knowledge Management

PB - Association for Computing Machinery

Y2 - 6 November 2017 through 10 November 2017

ER -