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

Majlesi Journal of Electrical Engineering

مقاله 10، دوره 18، شماره 1، خرداد 2024، صفحه 105-115   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2023.2004331.1339

نویسندگان

Kirti Sharma¹؛ Pawan Tiwari^{* 2}؛ Sanjay Kumar Sinha³

¹Department of Physics, Birla Institute of Technology, Mesra, Ranchi, India

²Department of Physics, Birla Institute of Technology Mesra, Ranchi, India.

³Department of Physics, Birla Institute of Technology Mesra, Ranchi, India

چکیده

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.

کلیدواژه‌ها

Estimation؛ Correlation؛ Analysis؛ Regression؛ Healthcare؛ Enlightenment؛ Machine learning؛ Quantum leap؛ Data Mining؛ Insights

مراجع

• S. (Dhaya) Battina, “The Role of Machine Learning in Clinical Research: Transforming the Future of Evidence Generation,” International Journal of Innovations in Engineering Research and Technology, vol. 4, no. 12, pp. 1–10, 2017, Accessed: Aug. 15, 2023. [Online]. Available: https://www.neliti.com/publications/429484/
• Chaudhuri, D. Oudejans, H. J. Thompson, and G. Demiris, “Real World Accuracy and Use of a Wearable Fall Detection Device by Older Adults,” J Am Geriatr Soc, vol. 63, no. 11, p. 2415, Nov. 2015, doi: 10.1111/JGS.13804.
• G. Dietterich, “Editorial Exploratory research in machine learning,” Machine Learning 1990 5:1, vol. 5, no. 1, pp. 5–9, Mar. 1990, doi: 10.1007/BF00115892.
• Chowriappa, S. Dua, and Y. Todorov, “Introduction to machine learning in healthcare informatics,” Intelligent Systems Reference Library, vol. 56, pp. 1–23, 2014, doi: 10.1007/978-3-642-40017-9_1.
• Kalaiselvi and M. Deepika, “Machine Learning for Healthcare Diagnostics,” pp. 91–105, 2020, doi: 10.1007/978-3-030-40850-3_5.
• “(PDF) Analysis of Medical Images Using Machine Learning.” https://www.researchgate.net/publication/370444441_Analysis_of_Medical_Images_Using_Machine_Learning (accessed Aug. 15, 2023).
• Wernick, Y. Yang, J. Brankov, G. Yourganov, and S. Strother, “Machine learning in medical imaging,” IEEE Signal Process Mag, vol. 27, no. 4, pp. 25–38, 2010, doi: 10.1109/MSP.2010.936730.
• W. Libbrecht and W. S. Noble, “Machine learning in genetics and genomics,” Nat Rev Genet, vol. 16, no. 6, p. 321, May 2015, doi: 10.1038/NRG3920.
• Landi et al., “Deep representation learning of electronic health records to unlock patient stratification at scale,” npj Digital Medicine 2020 3:1, vol. 3, no. 1, pp. 1–11, Jul. 2020, doi: 10.1038/s41746-020-0301-z.
• J. Gogtay, R. Ravi, and U. M. Thatte, “Regulatory requirements for clinical trials in India: What academicians need to know,” Indian J Anaesth, vol. 61, no. 3, p. 192, Mar. 2017, doi: 10.4103/IJA.IJA_143_17.
• Qayyum, J. Qadir, M. Bilal, and A. Al-Fuqaha, “Secure and Robust Machine Learning for Healthcare: A Survey,” IEEE Rev Biomed Eng, vol. 14, pp. 156–180, Jan. 2020, doi: 10.1109/RBME.2020.3013489.
• Shah et al., “Artificial intelligence and machine learning in clinical development: a translational perspective,” NPJ Digit Med, vol. 2, no. 1, Dec. 2019, doi: 10.1038/S41746-019-0148-3.
• Gusev et al., “Review Article Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability,” 2020, doi: 10.1155/2020/9628281.
• “(PDF) BGEM^TM: Assessing Elevated Blood Glucose Levels Using Machine Learning and Wearable Photo plethysmography Sensors.” https://www.researchgate.net/publication/366443831_BGEM_Assessing_Elevated_Blood_Glucose_Levels_Using_Machine_Learning_and_Wearable_Photo_plethysmography_Sensors (accessed Aug. 15, 2023).
• “Bio-Resource: How does glucometer or Glucose monitoring device work?” http://technologyinscience.blogspot.com/2015/01/how-does-glucometer-or-glucose.html#.Y_2WdHZBy5d (accessed Feb. 28, 2023).
• Wang et al., “Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier,” BMC Med Inform Decis Mak, vol. 21, no. 1, Dec. 2021, doi: 10.1186/S12911-021-01471-4.
• Tang, J. H. Lee, R. F. Louie, and G. J. Kost, “Effects of Different Hematocrit Levels on Glucose Measurements With Handheld Meters for Point-of-Care Testing,” Arch Pathol Lab Med, vol. 124, no. 8, pp. 1135–1140, Aug. 2000, doi: 10.5858/2000-124-1135-EODHLO.
• Sittampalam and G. S. Wilson, “Surface-modified electrochemical detector for liquid chromatography,” 2002, doi: 10.1021/AC00260A039.
• N. Szentirmay and C. R. Martin, “Ion-Exchange Selectivity of Nafion Films on Electrode Surfaces,” Anal Chem, vol. 56, no. 11, pp. 1898–1902, 1984, doi: 10.1021/AC00275A031/ASSET/AC00275A031.FP.PNG_V03.
• “Biochemistry Analyzer: Applications, Facts, and Benefits – Blog | Trivitron Healthcare Solutions | Medical Device Company.” https://www.trivitron.com/blog/biochemistry-analyzer-applications-facts-and-benefits/ (accessed Feb. 28, 2023).
• AHeller and B. Feldman, “Electrochemical glucose sensors and their applications in diabetes management,” Chem Rev, vol. 108, no. 7, pp. 2482–2505, Jul. 2008, doi: 10.1021/CR068069Y/ASSET/IMAGES/CR-2006-08069Y_M011.GIF.
• K. Tiwari, H. Nam, and Y. S. Sohn, “Correlation between blood glucose and hematocrit: A new estimation methodology,” Biochip J, vol. 6, no. 3, pp. 206–212, Sep. 2012, doi: 10.1007/S13206-012-6302-5/METRICS.
• Hönes, P. Müller, and N. Surridge, “The Technology Behind Glucose Meters: Test Strips,” https://home.liebertpub.com/dia, vol. 10, no. SUPPL. 1, May 2008, doi: 10.1089/DIA.2008.0005.
• Wang, “Electrochemical glucose biosensors,” Chem Rev, vol. 108, no. 2, pp. 814–825, Feb. 2008, doi: 10.1021/CR068123A.
• Heller and B. Feldman, “Electrochemical glucose sensors and their applications in diabetes management,” Chem Rev, vol. 108, no. 7, pp. 2482–2505, Jul. 2008, doi: 10.1021/CR068069Y/ASSET/IMAGES/LARGE/CR-2006-08069Y_0001.JPEG.
• Amine, J. M. Kauffmann, G. J. Patriarche, and G. G. Guilbault, “Electrochemical Behaviour of a Lipid Modified Enzyme Electrode,” http://dx.doi.org/10.1080/00032718908052362, vol. 22, no. 11–12, pp. 2403–2411, 2006, doi: 10.1080/00032718908052362.
• J. Khanam and S. Y. Foo, “A comparison of machine learning algorithms for diabetes prediction,” ICT Express, vol. 7, no. 4, pp. 432–439, Dec. 2021, doi: 10.1016/J.ICTE.2021.02.004.
• Plis, R. Bunescu, C. Marling, J. Shubrook, and F. Schwartz, “A Machine Learning Approach to Predicting Blood Glucose Levels for Diabetes Management”, Accessed: Aug. 15, 2023. [Online]. Available: www.aaai.org