Support Vector and Linear Regression Machine Learning Model on Amperometric Signals to Predict Glucose Concentration and Hematocrit Volume
Publish place: majlesi Journal of Electrical Engineering، Vol: 18، Issue: 1
Publish Year: 1403
نوع سند: مقاله ژورنالی
زبان: English
View: 21
This Paper With 11 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_MJEE-18-1_010
تاریخ نمایه سازی: 9 اردیبهشت 1403
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 R۲ score for GC is approximately ۰.۹۱۶, whereas for HV, it reaches around ۰.۵۳۷. In contrast, employing the support vector regressor yielded R۲ scores of about ۰.۹۶۱ for GC and ۰.۵۰۶ for HV.
Keywords:
Authors
Kirti Sharma
Department of Physics, Birla Institute of Technology, Mesra, Ranchi, India
Pawan Tiwari
Department of Physics, Birla Institute of Technology Mesra, Ranchi, India.
Sanjay Sinha
Department of Physics, Birla Institute of Technology Mesra, Ranchi, India
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :