@inproceedings{9329969858ca4af1896d96d7f08fe2c0,
title = "Prediction of medical examination results using radial-basis function networks",
abstract = "This paper proposes a method of predicting future medical examination measurements given the past values. The medical examinations considered in this paper are blood sugar level, low and high blood pressures, and cholesterol level. This paper uses a specific type of artificial neural networks, radialbasis function network (RBFN), to approximate mapping from the past medical measurements to that of the upcoming year, in order to help the subjects be aware of the signs of unusual health states without consulting with doctors. Experimental results show that the RBFN-based estimation is superior to the conventional linear regression in terms of prediction accuracy of the future examination measurements. The proposed method is expected to be implemented in a handy consumer electronic devices such as Smartphones without adding extra hardware parts provided that the history of the medical examination measurements are available.",
keywords = "Linear regression, Medical examination, Radial-basis function networks",
author = "Jang, {Gil Jin} and Minho Kim and Kim, {Young Won} and Jaehun Choi",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2016 ; Conference date: 26-10-2016 Through 28-10-2016",
year = "2017",
month = jan,
day = "3",
doi = "10.1109/ICCE-Asia.2016.7804745",
language = "English",
series = "2016 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2016",
address = "United States",
}