Support Vector and Linear Regression Machine Learning Model on Amperometric Signals to Predict Glucose Concentration and Hematocrit Volume

Abstract

Data represents a compendium of information that perpetually expands with each passing moment, contributed by individuals worldwide. Within the domain of medical science, this reservoir of data accumulates at an almost exponential rate, doubling in volume annually. The emergence of advanced machine learning tools and techniques, subsequent to a substantial evolution in data mining strategies, has bestowed the capacity to glean insights and discern concealed patterns from vast datasets, thus enabling extensive analytical pursuits. This study delves into the application of machine learning algorithms to enhance societal well-being by harnessing the transformative potential of machine learning advancements in the domain of blood glucose concentration estimation through regression analysis. The culmination of this investigation involves establishing a correlation between glucose concentration and hematocrit volume. The dataset employed for this research is sourced from clinically validated electrochemical glucose sensors (commonly referred to as glucose strips). It encompasses diverse levels of both glucose concentration and hematocrit volume, the latter being furnished by an undisclosed source to ensure copyright compliance. This dataset comprises four distinct variables, and the aim of this research involves training the dataset using regression techniques to predict two of these variables. Our results indicate that when utilizing linear regression, the R2 score for GC is approximately 0.916, whereas for HV, it reaches around 0.537. In contrast, employing the support vector regressor yielded R2 scores of about 0.961 for GC and 0.506 for HV.

 

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