#This project aims at predicting blood glucose levels based on NIR spectroscopic response data utilizing machine learning techniques.
#Blood glucose samples were prepared in a controlled environment and the NIR spectrums of the samples were obtained using NeoSpectraMicro development kit.
#Two machine learning approaches have been employed to analyze the experimental dataset. Firstly, the Random Forest Algorithm (RF) followed by Support Vector Machine (SVM) has been utilized that provides an accuracy of 67.5%. Then, a combination of Principle Component Analysis (PCA) and SVM is used. PCA followed by SVM shows a promising result of 77.5% accuracy compared to the previous technique.
#The numerical findings reveal that the NIR spectroscopy with appropriate data modeling algorithm can be a potential candidate for non-invasive blood glucose monitoring system.