Comparison of Machine Learning Algorithm For Urine Glucose Level Classification Using Side-Polished Fiber Sensor

Abstract

Urine glucose levels can be used to determine if glucose levels in the human body are too high, which may be a sign of diabetes. A non-invasive urine glucose classification model was conducted by using of the color of urine after benedict reaction to measure the level of glucose. The aim of this study is to classification urine glucose levels from a side-polished fiber sensor performed by using machine learning algorithms to get the best algorithm performance. By removing the coating and cladding this sensor is made of a polymer optical fiber. The measurement is focused on changes in the cladding refractive index which affects the amount of light transmitted. The machine learning system has been implemented using the Naïve Bayes Classifier, k-Nearest Neighbor Classifier, Logistic Regression, Random Forest, Artificial Neural Networks and Support Vector Machine. The measurement data on samples were collected from previous studies of a total of 120 urine samples for testing in this study. The results of the experiments performed with k-fold cross validation show that the neural network gets the accuracy results of 96.7%, the value of precision 0.967, recall 0.967, and F1-Measure 0.967. With cross validation leave-one-out, the experimental results show the classification algorithm with the best accuracy value that is at the random forest and artificial neural networks 0.975, precision 0.975, recall 0975, and F1-Measure 0.975. While the ANN algorithm is superior in achieving an accuracy value of 98.6%. Therefore, artificial neural networks are the best method for classifying glucose levels in the human body for fasting and postprandial urine tests.