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@article{ART002419475},
author={Jin-Gyo Jeong and Myung-Suk Lee},
title={A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2018},
volume={23},
number={12},
pages={131-136},
doi={10.9708/jksci.2018.23.12.131}

TY - JOUR
AU - Jin-Gyo Jeong
AU - Myung-Suk Lee
TI - A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning
JO - Journal of The Korea Society of Computer and Information
PY - 2018
VL - 23
IS - 12
PB - The Korean Society Of Computer And Information
SP - 131
EP - 136
SN - 1598-849X
AB - This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.
KW - Machine Learning;Jaundice;Computer-aided Diagnosis;Non-invasive
DO - 10.9708/jksci.2018.23.12.131
ER -

Jin-Gyo Jeong and Myung-Suk Lee. (2018). A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning. Journal of The Korea Society of Computer and Information, 23(12), 131-136.

Jin-Gyo Jeong and Myung-Suk Lee. 2018, "A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning", Journal of The Korea Society of Computer and Information, vol.23, no.12 pp.131-136. Available from: doi:10.9708/jksci.2018.23.12.131

Jin-Gyo Jeong, Myung-Suk Lee "A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning" Journal of The Korea Society of Computer and Information 23.12 pp.131-136 (2018) : 131.

Jin-Gyo Jeong, Myung-Suk Lee. A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning. 2018; 23(12), 131-136. Available from: doi:10.9708/jksci.2018.23.12.131

Jin-Gyo Jeong and Myung-Suk Lee. "A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning" Journal of The Korea Society of Computer and Information 23, no.12 (2018) : 131-136.doi: 10.9708/jksci.2018.23.12.131

Jin-Gyo Jeong; Myung-Suk Lee. A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning. Journal of The Korea Society of Computer and Information, 23(12), 131-136. doi: 10.9708/jksci.2018.23.12.131

Jin-Gyo Jeong; Myung-Suk Lee. A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning. Journal of The Korea Society of Computer and Information. 2018; 23(12) 131-136. doi: 10.9708/jksci.2018.23.12.131

Jin-Gyo Jeong, Myung-Suk Lee. A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning. 2018; 23(12), 131-136. Available from: doi:10.9708/jksci.2018.23.12.131

Jin-Gyo Jeong and Myung-Suk Lee. "A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning" Journal of The Korea Society of Computer and Information 23, no.12 (2018) : 131-136.doi: 10.9708/jksci.2018.23.12.131