Non-Invasive Hemoglobin Monitoring Device using K-Nearest Neighbor and Artificial Neural Network Back Propagation Algorithms

Abstract— The invasive method of medically checking hemoglobin level in human body by taking the blood sample of the patient requiring a long time and injuring the patient is seen impractical. A non-invasive method of measuring hemoglobin levels, therefore, is made by applying the K-Nearest Neighbor (KNN) algorithm and the Artificial Neural Network Back Propagation (ANN-BP) algorithm with the Internet of Things-based HTTP protocol to achieve the high accuracy and the low end-to-end delay. Based on tests conducted on a Noninvasive Hemoglobin measuring device connected to Cloud Things Speak, the prediction process using algorithm by means of Python programming based on Android application could work well. The result of this study showed that the accuracy of the K-Nearest Neighbor algorithm was 94.01%; higher than that of the Artificial Neural Network Back Propagation algorithm by 92.45%. Meanwhile, the end-to-end delay was at 6.09 seconds when using the KNN algorithm and at 6.84 seconds when using Artificial Neural Network Back Propagation Algorithm