TY - GEN
T1 - Identification of Biomarkers with Different Classifiers in Urine Test∗
AU - Zhang, Haotian
AU - Dong, Tao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Biomarkers in urine samples are widely used in clinical diagnosis. Involving image processing and data analysis, urinalysis is very popular in hospitals because of its convenience and speediness; and the most important reason is its high accuracy rating. This paper presents colorimetric recognition for urine test device with different algorithms aiming to find a good-performance classifier. Those algorithms can train a set of data and get a model to discriminate the test data. Almost the accuracy of each classifier is beyond 92%, even 99%. Although the classifier that has highest average accurate rate of recognition is K-Nearest Neighbor, we cannot overlook the performance of Support Vector Machine, which perform best in protein test. In order to compare these eight algorithms, we use Python simulation to validate the results and show the accuracy of each classifier.
AB - Biomarkers in urine samples are widely used in clinical diagnosis. Involving image processing and data analysis, urinalysis is very popular in hospitals because of its convenience and speediness; and the most important reason is its high accuracy rating. This paper presents colorimetric recognition for urine test device with different algorithms aiming to find a good-performance classifier. Those algorithms can train a set of data and get a model to discriminate the test data. Almost the accuracy of each classifier is beyond 92%, even 99%. Although the classifier that has highest average accurate rate of recognition is K-Nearest Neighbor, we cannot overlook the performance of Support Vector Machine, which perform best in protein test. In order to compare these eight algorithms, we use Python simulation to validate the results and show the accuracy of each classifier.
UR - https://www.scopus.com/pages/publications/85056599646
U2 - 10.1109/EMBC.2018.8513030
DO - 10.1109/EMBC.2018.8513030
M3 - 会议稿件
C2 - 30441008
AN - SCOPUS:85056599646
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2905
EP - 2908
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -