TY - GEN
T1 - Profiling health prevention population for hypertension screening and ECG test rationing
AU - Herazo-Padilla, Nilson
AU - Augusto, Vincent
AU - Bongue, Bienvenu
AU - Xie, Xiaolan
N1 - Funding Information:
* This work is supported by the CETAF.
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - This paper addresses the question of whether ECG test is needed for hypertension screening for all subjects of preventive health checkup. For this purpose, we propose a decision tree approach for subject profiling depending on their characteristics and results of medical exams. The population of hypertension subjects being too small with 1% of the whole, learning sets with higher hypertension population are proposed to enhance the decision tree approach. The decision tree allows identifying subject groups for which ECG is needed. Numerical experiments with historical data from CES-Saint Etienne show a correct classification probability of 96% of hypertension subjects and a drastic reduction of 98% ECG tests. Last but not the least, the resulting decision tree is implementable in practice.
AB - This paper addresses the question of whether ECG test is needed for hypertension screening for all subjects of preventive health checkup. For this purpose, we propose a decision tree approach for subject profiling depending on their characteristics and results of medical exams. The population of hypertension subjects being too small with 1% of the whole, learning sets with higher hypertension population are proposed to enhance the decision tree approach. The decision tree allows identifying subject groups for which ECG is needed. Numerical experiments with historical data from CES-Saint Etienne show a correct classification probability of 96% of hypertension subjects and a drastic reduction of 98% ECG tests. Last but not the least, the resulting decision tree is implementable in practice.
UR - https://www.scopus.com/pages/publications/85059985422
UR - https://www.scopus.com/pages/publications/85059985422#tab=citedBy
U2 - 10.1109/COASE.2018.8560601
DO - 10.1109/COASE.2018.8560601
M3 - Conference contribution
AN - SCOPUS:85059985422
T3 - IEEE International Conference on Automation Science and Engineering
SP - 371
EP - 377
BT - 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
PB - IEEE Computer Society
T2 - 14th IEEE International Conference on Automation Science and Engineering, CASE 2018
Y2 - 20 August 2018 through 24 August 2018
ER -