Identification of Biomarkers with Different Classifiers in Urine Test

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.