Login Register Cart 0
Generic placeholder image

Current Biotechnology

Editor-in-Chief

ISSN (Print): 2211-5501
ISSN (Online): 2211-551X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Futuristic Approach to Cholesterol Detection by Utilizing Non-invasive Techniques

Author(s): Mithra Geetha, Kishor Kumar Sadasivuni*, Somaya Al-Maadeed, Asan G.A. Muthalif, Sajna M.S and Mizaj Shabil Sha

Volume 12, Issue 2, 2023

Published on: 15 May, 2023

Page: [79 - 93] Pages: 15

DOI: 10.2174/2211550112666230419110914

Price: $65

conference banner
Abstract

Regular blood cholesterol control is an integral part of healthcare for detecting cardiovascular issues immediately. Existing procedures are mostly intrusive and necessitate the collection of blood samples. Furthermore, because of the danger of infection, bruising, and/or haematoma, this measurement method may not be appropriate for continuous or regular examinations. As a result, an alternate option is required, which is known as the noninvasive (NI) approach that does not necessitate the collection of blood samples. Because NI approaches give painless and precise answers, they can be used in place of intrusive procedures. This review article includes a comprehensive investigation on NI methodologies and various NI approaches for detecting cholesterol in the bloodstream. It is important to note that medical system possibilities are changing due to the algorithms for NI techniques, which ultimately project the need for patient monitoring via the internet of medical things (IoMT) and artificial intelligence (AI).

Keywords: Noninvasive, minimally invasive, spectroscopy, continuous monitoring, cholesterol biosensors, cholesterol sensor, near-infrared light, image processing.

« Previous
Graphical Abstract

[1]
Aristovich E, Khan S. Non-invasive measurement of cholesterol in human blood by impedance technique: An investigation by 3D finite element field modelling. J Phys Conf Ser 2013; 450(1): 012057.
[http://dx.doi.org/10.1088/1742-6596/450/1/012057]
[2]
Law E, Kakani M, Agarwal M, Rizkalla M. Electromagnetic simulation for the diagnosis of lipoprotein density in human blood, a non-invasive approach. Open J Appl Biosensor 2017; 4(1): 1-11.
[http://dx.doi.org/10.4236/ojab.2017.41001]
[3]
Nantaphol S, Chailapakul O, Siangproh W. Sensitive and selective electrochemical sensor using silver nanoparticles modified glassy carbon electrode for determination of cholesterol in bovine serum. Sensors Actuators B Chem 2015; 207(A): 193-8.
[http://dx.doi.org/10.1016/j.snb.2014.10.041]
[4]
Li L, Wang Y, Pan L, et al. A nanostructured conductive hydrogels-based biosensor platform for human metabolite detection. Nano Lett 2015; 15(2): 1146-51.
[http://dx.doi.org/10.1021/nl504217p] [PMID: 25569673]
[5]
Sozen E, Ozer NK. Impact of high cholesterol and endoplasmic reticulum stress on metabolic diseases: An updated mini-review. Redox Biol 2017; 12: 456-61.
[http://dx.doi.org/10.1016/j.redox.2017.02.025] [PMID: 28319895]
[6]
dos CE, Ferreira S, França CN. Clinical correlation between a point-of-care testing system and laboratory automation for lipid profile. Clin Chim Acta 2015; 446: 263-6.
[http://dx.doi.org/10.1016/j.cca.2015.04.036]
[7]
Scheuer C. Disentangling inclusion in physical education lessons: Developing a resource toolkit for teachers. Phys Educ Sport Child Youth with Spec Needs Res-Best Pract- Situat. 2021; 343-54.
[http://dx.doi.org/10.2/JQUERY.MIN.JS]
[8]
Mazalli MR, Sawaya ACHF, Eberlin MN, Bragagnolo N. HPLC method for quantification and characterization of cholesterol and its oxidation products in eggs. Lipids 2006; 41(6): 615-22.
[http://dx.doi.org/10.1007/s11745-006-5010-0] [PMID: 16981439]
[9]
Chitra J, Ghosh M, Mishra HN. Rapid quantification of cholesterol in dairy powders using Fourier transform near infrared spectroscopy and chemometrics. Food Control 2017; 78: 342-9.
[http://dx.doi.org/10.1016/j.foodcont.2016.10.008]
[10]
Li J, Liu T, Liu S, Li J, Huang G, Yang HH. Bifunctional magnetic nanoparticles for efficient cholesterol detection and elimination via host-guest chemistry in real samples. Biosens Bioelectron 2018; 120: 137-43.
[http://dx.doi.org/10.1016/j.bios.2018.08.046] [PMID: 30195087]
[11]
Thakur N, Kumar M, Das Adhikary S, Mandal D, Nagaiah TC. PVIM–Co 5 POM/MNC composite as a flexible electrode for the ultrasensitive and highly selective non-enzymatic electrochemical detection of cholesterol. Chem Commun 2019; 55(34): 5021-4.
[http://dx.doi.org/10.1039/C9CC01534E] [PMID: 30968895]
[12]
Nirala NR, Pandey S, Bansal A, et al. Different shades of cholesterol: Gold nanoparticles supported on MoS2 nanoribbons for enhanced colorimetric sensing of free cholesterol. Biosens Bioelectron 2015; 74: 207-13.
[http://dx.doi.org/10.1016/j.bios.2015.06.043] [PMID: 26143460]
[13]
Devi MS, Karthikeyan B, Gnanamoorthy G, Srinivasan S. Green synthesis of core - Shell Te–Se bimetallic nanoparticles using Cinnamomum Camphora leaf extract and their in-vitro cholesterol degradation. Opt Mater 2022; 128: 112375.
[http://dx.doi.org/10.1016/j.optmat.2022.112375]
[14]
Xu J, Jiang D, Qin Y, Xia J, Jiang D, Chen HY. C 3 N 4 nanosheet modified microwell array with enhanced electrochemiluminescence for total analysis of cholesterol at single cells. Anal Chem 2017; 89(4): 2216-20.
[http://dx.doi.org/10.1021/acs.analchem.6b04635] [PMID: 28192948]
[15]
Qing Z, Bai A, Xing S, et al. Progress in biosensor based on DNA-templated copper nanoparticles. Biosens Bioelectron 2019; 137: 96-109.
[http://dx.doi.org/10.1016/j.bios.2019.05.014] [PMID: 31085403]
[16]
Song C, Hong W, Zhang X, Lu Y. Label-free and sensitive detection of Ochratoxin A based on dsDNA-templated copper nanoparticles and exonuclease-catalyzed target recycling amplification. Analyst 2018; 143(8): 1829-34.
[http://dx.doi.org/10.1039/C8AN00158H] [PMID: 29594306]
[17]
Qing Z, Xu J, Hu J, et al. In situ amplification‐based imaging of RNA in living cells. Angew Chem Int Ed 2019; 58(34): 11574-85.
[http://dx.doi.org/10.1002/anie.201812449] [PMID: 30707484]
[18]
Huang S, Yang E, Yao J, et al. Nitrogen, Cobalt Co-doped Fluorescent Magnetic Carbon dots as ratiometric fluorescent probes for cholesterol and uric acid in human blood serum. ACS Omega 2019; 4(5): 9333-42.
[http://dx.doi.org/10.1021/acsomega.9b00874] [PMID: 31460022]
[19]
Chang HC, Ho JA. Gold nanocluster-assisted fluorescent detection for hydrogen peroxide and cholesterol based on the inner filter effect of gold nanoparticles. Anal Chem 2015; 87(20): 10362-7.
[http://dx.doi.org/10.1021/acs.analchem.5b02452] [PMID: 26379119]
[20]
Zhang BL, Zhang XP, Chen BZ, Fei WM, Cui Y, Guo XD. Microneedle-assisted technology for minimally invasive medical sensing. Microchem J 2021; 162: 105830.
[http://dx.doi.org/10.1016/j.microc.2020.105830]
[21]
Jankowska K, Sigurdardóttir SB, Zdarta J, Pinelo M. Co-immobilization and compartmentalization of cholesterol oxidase, glucose oxidase and horseradish peroxidase for improved thermal and H2O2 stability. J Membr Sci 2022; 662: 121007.
[http://dx.doi.org/10.1016/j.memsci.2022.121007]
[22]
Quispe R, Hendrani A, Elshazly MB, et al. Accuracy of low-density lipoprotein cholesterol estimation at very low levels. BMC Med 2017; 15(1): 83.
[http://dx.doi.org/10.1186/s12916-017-0852-2] [PMID: 28427464]
[23]
Myers GL, Kimberly MM, Waymack PP, Smith SJ, Cooper GR, Sampson EJ. A reference method laboratory network for cholesterol: A model for standardization and improvement of clinical laboratory measurements. Clin Chem 2000; 46(11): 1762-72.
[http://dx.doi.org/10.1093/clinchem/46.11.1762] [PMID: 11067811]
[24]
Butovich IA, Lu H, McMahon A, Eule JC. Toward an animal model of the human tear film: Biochemical comparison of the mouse, canine, rabbit, and human meibomian lipidomes. Invest Ophthalmol Vis Sci 2012; 53(11): 6881-96.
[http://dx.doi.org/10.1167/iovs.12-10516] [PMID: 22918629]
[25]
Mariutti LRB, Nogueira GC, Bragagnolo N. Optimization and validation of analytical conditions for cholesterol and cholesterol oxides extraction in chicken meat using response surface methodology. J Agric Food Chem 2008; 56(9): 2913-8.
[http://dx.doi.org/10.1021/jf0735432] [PMID: 18419125]
[26]
Li R, Xiong C, Xiao Z, Ling L. Colorimetric detection of cholesterol with G-quadruplex-based DNAzymes and ABTS2. Anal Chim Acta 2012; 724: 80-5.
[http://dx.doi.org/10.1016/j.aca.2012.02.015] [PMID: 22483213]
[27]
Qureshi RN, Kok WT, Schoenmakers PJ. Fractionation of human serum lipoproteins and simultaneous enzymatic determination of cholesterol and triglycerides. Anal Chim Acta 2009; 654(1): 85-91.
[http://dx.doi.org/10.1016/j.aca.2009.06.060] [PMID: 19850173]
[28]
Ji-gui W, Xiao-shu X. Electrophoresis and the traditional method for the measurement of lipoprotein cholesterol in serum. Hunan Yi Ke Da Xue Xue Bao 2022; 27(1): 51-4. Available from: https://pubmed.ncbi.nlm.nih.gov/12575236
[29]
Allain CC, Poon LS, Chan CSG, Richmond W, Fu PC. Enzymatic determination of total serum cholesterol. Clin Chem 1974; 20(4): 470-5.
[http://dx.doi.org/10.1093/clinchem/20.4.470] [PMID: 4818200]
[30]
Umar U, Syarif S, Nurtanio I, et al. A real time noninvasive cholesterol monitoring system. International Conference on Urban Disaster Resilience (ICUDR 2019). 331: 10.
[http://dx.doi.org/10.1051/matecconf/202033106005]
[31]
Warnick G. Measurement of cholesterol in plasma and other body fluids. Curr Atheroscler Rep 2001; 3(5): 404-11.
[http://dx.doi.org/10.1007/s11883-001-0079-7]
[32]
(2022) A Novel Test for the Measurement of Skin Cholesterol, Clinical Chemistry | DeepDyve. 2022. Available From: https://www.deepdyve.com/lp/american-association-for-clinical-chemistry/a-novel-test-for-the-measurement-of-skin-cholesterol-j3oXXkdhzZ
[33]
Sprecher DL, Evelegh MJ, Norton B, et al. Skin cholesterol A new method to predict angiographic disease. Circulation 2000; 102(S18): 857. Available from: https://eurekamag.com/research/035/738/035738198.php
[34]
Ni J, Hong H, Zhang Y, et al. Development of a non-invasive method for skin cholesterol detection: Pre-clinical assessment in atherosclerosis screening. Biomed Eng Online 2021; 20(1): 52.
[http://dx.doi.org/10.1186/s12938-021-00889-1] [PMID: 34074299]
[35]
Lai J, Han Y, Huang C, et al. Non-invasive skin cholesterol testing: A potential proxy for LDL-C and apoB serum measurements. Lipids Health Dis 2021; 20(1): 137.
[http://dx.doi.org/10.1186/s12944-021-01571-0] [PMID: 34657601]
[36]
Wu P. Rapid noninvasive detection technology of skin cholesterol based on fluorescence spectroscopy. Chin J Lasers 2021; 48(3): 0307002.
[http://dx.doi.org/10.3788/CJL202148.0307002]
[37]
Ilea A, Andrei V, Feurdean C, et al. Saliva, a magic biofluid available for multilevel assessment and a mirror of general health-A systematic review. Biosensors 2019; 9(1): 27.
[http://dx.doi.org/10.3390/bios9010027] [PMID: 30769890]
[38]
Cox RA, García-Palmieri MR. Cholesterol, triglycerides, and associated lipoproteins. Clin Methods Hist Phys Lab Exam 1990. Available from: https://www.ncbi.nlm.nih.gov/books/NBK351/
[39]
Singh S, Ramesh V, Oza N, Balamurali P, Prashad K, Balakrishnan P. Evaluation of serum and salivary lipid profile: A correlative study. J Oral Maxillofac Pathol 2014; 18(1): 4-8.
[http://dx.doi.org/10.4103/0973-029X.131881] [PMID: 24959029]
[40]
Larsson B, Olivecrona G, Ericson T. Lipids in human saliva. Arch Oral Biol 1996; 41(1): 105-10.
[http://dx.doi.org/10.1016/0003-9969(95)00077-1] [PMID: 8833598]
[41]
Karjalainen S, Sewón L, Söderling E, et al. Salivary cholesterol of healthy adults in relation to serum cholesterol concentration and oral health. J Dent Res 1997; 76(10): 1637-43.
[http://dx.doi.org/10.1177/00220345970760100401] [PMID: 9326895]
[42]
Eom KS, Lee YJ, Seo HW, Kang JY, Shim JS, Lee SH. Sensitive and non-invasive cholesterol determination in saliva via optimization of enzyme loading and platinum nano-cluster composition. Analyst 2020; 145(3): 908-16.
[http://dx.doi.org/10.1039/C9AN01679A] [PMID: 31820750]
[43]
Singh S, Solanki PR, Pandey MK, Malhotra BD. Cholesterol biosensor based on cholesterol esterase, cholesterol oxidase and peroxidase immobilized onto conducting polyaniline films. Sens Actuators B Chem 2006; 115(1): 534-41.
[http://dx.doi.org/10.1016/j.snb.2005.10.025]
[44]
Lin X, Ni Y, Kokot S. Electrochemical cholesterol sensor based on cholesterol oxidase and MoS2-AuNPs modified glassy carbon electrode. Sens Actuators B Chem 2016; 233: 100-6.
[http://dx.doi.org/10.1016/j.snb.2016.04.019]
[45]
Soylemez S, Udum YA, Kesik M. Gündoğdu Hızlıateş C, Ergun Y, Toppare L. Electrochemical and optical properties of a conducting polymer and its use in a novel biosensor for the detection of cholesterol. Sens Actuators B Chem 2015; 212: 425-33.
[http://dx.doi.org/10.1016/j.snb.2015.02.045]
[46]
Jena BK, Raj CR. Enzyme integrated silicate–Pt nanoparticle architecture: A versatile biosensing platform. Biosens Bioelectron 2011; 26(6): 2960-6.
[http://dx.doi.org/10.1016/j.bios.2010.11.046] [PMID: 21177093]
[47]
Lee YJ, Eom KS, Shin KS, Kang JY, Lee SH. Enzyme-loaded paper combined impedimetric sensor for the determination of the low-level of cholesterol in saliva. Sens Actuators B Chem 2018; 271: 73-81.
[http://dx.doi.org/10.1016/j.snb.2018.05.080]
[48]
(2021) Implantable Sensor Systems for Medical Applications. 1st Ed. 2021. Available From: https://www.elsevier.com/books/implantable-sensor-systems-for-medical-applications/inmann/978-1-84569-987-1
[49]
Guilbault G, Palleschi G, Lubrano G. Non-invasive biosensors in clinical analysis. Biosens Bioelectron 1995; 10(3-4): 379-92.
[http://dx.doi.org/10.1016/0956-5663(95)96856-T] [PMID: 7755964]
[50]
Pappa AM, Curto VF, Braendlein M, et al. Organic transistor arrays integrated with finger-powered microfluidics for multianalyte saliva testing. Adv Healthc Mater 2016; 5(17): 2295-302.
[http://dx.doi.org/10.1002/adhm.201600494] [PMID: 27385673]
[51]
Lindenthal B, Simatupang A, Dotti MT, Federico A, Lütjohann D, von Bergmann K. Urinary excretion of mevalonic acid as an indicator of cholesterol synthesis. J Lipid Res 1996; 37(10): 2193-201.
[http://dx.doi.org/10.1016/S0022-2275(20)37301-6] [PMID: 8906596]
[52]
Nozaki S. Effects of pravastatin on plasma and urinary mevalonate concentrations in subjects with familial hypercholesterolaemia: A comparison of morning and evening administration. J Clin Pharmacol 1996; 49(5): 361-4.
[http://dx.doi.org/10.1007/BF00203778]
[53]
Versura P. Tear proteomics in evaporative dry eye disease. Eye 2010; 24(8): 1396-402.
[http://dx.doi.org/10.1038/eye.2010.7]
[54]
Human tear cholesterol levels. 2021. Available From: https://pubmed.ncbi.nlm.nih.gov/132920
[55]
Mudgil P, Dennis GR, Millar TJ. Interactions of poly(tert-butyl acrylate)-poly(styrene) diblock copolymers with lipids at the air-water interface. Langmuir 2006; 22(18): 7672-7.
[http://dx.doi.org/10.1021/la060515p] [PMID: 16922549]
[56]
Arciniega JC, Uchiyama E, Butovich IA. Disruption and destabilization of meibomian lipid films caused by increasing amounts of ceramides and cholesterol. Invest Ophthalmol Vis Sci 2013; 54(2): 1352-60.
[http://dx.doi.org/10.1167/iovs.12-10662] [PMID: 23341008]
[57]
Shine WE, McCulley JP, Pandya AG. Minocycline effect on meibomian gland lipids in meibomianitis patients. Exp Eye Res 2003; 76(4): 417-20.
[http://dx.doi.org/10.1016/S0014-4835(03)00005-8] [PMID: 12634106]
[58]
Wei XE, Korth J, Brown SHJ, et al. Rapid quantification of free cholesterol in tears using direct insertion/electron ionization-mass spectrometry. Invest Ophthalmol Vis Sci 2013; 54(13): 8027-35.
[http://dx.doi.org/10.1167/iovs.13-12786] [PMID: 24255037]
[59]
Tamura T, Eda H, Takada M, Kubodera T. New instrument for monitoring hemoglobin oxygenation. Adv Exp Med Biol 1989; 248(103–107): 103-7.
[http://dx.doi.org/10.1007/978-1-4684-5643-1_13] [PMID: 2782136]
[60]
Aristovich E, Khan SH. Non-invasive measurement of cholesterol in human blood by impedance technique: An investigation by 2D finite element field modelling. J Phys Conf Ser 2013; 459(1): 012030.
[http://dx.doi.org/10.1088/1742-6596/459/1/012030]
[61]
Infrared cholesterol sensor. Patent US5246004A, 2021. Available From: https://patents.google.com/patent/US5246004A/en
[62]
Jaross W, Neumeister V, Lattke P, Schuh D. Determination of cholesterol in atherosclerotic plaques using near infrared diffuse reflection spectroscopy. Atherosclerosis 1999; 147(2): 327-37.
[http://dx.doi.org/10.1016/S0021-9150(99)00203-8] [PMID: 10559519]
[63]
Umar U, Syarif S, Nurtanio I. Development reflective optical sensor for blood cholesterol measurement using LED infrared 940 nm. Int J Eng Res Technol 2020; 13: 4899-907. Available from: http://www.irphouse.com
[64]
Shanker NR, Ezhil A, Archana S. Non-invasive method of detection of cholesterol using image processing. Int J Med Eng Inform 2012; 4(3): 223-30.
[http://dx.doi.org/10.1504/IJMEI.2012.048384]
[65]
Ramlee RA, Ranjit S. Using iris recognition algorithm, detecting cholesterol presence. Int Conf Inf Manag Eng ICIME 2009. 714-.
[http://dx.doi.org/10.1109/ICIME.2009.61]
[66]
Alhasawi Y, Mullachery B, Chatterjee S. Design of a mobile-app for noninvasively detecting high blood cholesterol using eye images. Proc Annu Hawaii Int Conf Syst Sci. 3227-5.
[http://dx.doi.org/10.24251/HICSS.2018.407]
[67]
Gruson D, Helleputte T, Rousseau P, Gruson D. Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation. Clin Biochem 2019; 69: 1-7.
[http://dx.doi.org/10.1016/j.clinbiochem.2019.04.013] [PMID: 31022391]
[68]
Mitchell TM. Machine learning and data mining. Commun ACM 1999; 42(11): 30-6.
[http://dx.doi.org/10.1145/319382.319388]
[69]
Chen PHC, Liu Y, Peng L. How to develop machine learning models for healthcare. Nat Mater 2019; 18(5): 410-4.
[http://dx.doi.org/10.1038/s41563-019-0345-0]
[70]
Artificial Intelligence: A Modern Approach 4th US ed.. 2021. Available From: http://aima.cs.berkeley.edu/
[71]
Sanchez-Pinto LN, Luo Y, Churpek MM. Big data and data science in critical care. Chest 2018; 154(5): 1239-48.
[http://dx.doi.org/10.1016/j.chest.2018.04.037] [PMID: 29752973]
[72]
Campbell C, Ying Y. Learning with support vector machines. Synth Lect Artif Intell 2011; 5(1): 1-95.
[http://dx.doi.org/10.2200/S00324ED1V01Y201102AIM010]
[73]
Hasan MAM, Nasser M, Ahmad S, Molla KI. Feature selection for intrusion detection using random forest. J Inf Secur 2016; 7(3): 129-40.
[http://dx.doi.org/10.4236/jis.2016.73009]
[74]
a) Kleinbaum D G, Klein M. Logistic Regression ;
b) Zhang P, Wang R, Xiu N. Multinomial logistic regression classifier via lq, 0-proximal Newton algorithm. Neurocomputing 2012; 468: 148-64.
[http://dx.doi.org/10.1007/978-1-4419-1742-3]
[75]
Vinuesa R, Brunton SL. Enhancing computational fluid dynamics with machine learning. Nat Comput Sci 2022; 2(6): 358-66.
[http://dx.doi.org/10.1038/s43588-022-00264-7]
[76]
Chen JH, Asch SM. Machine learning and prediction in medicine - beyond the peak of inflated expectations. N Engl J Med 2017; 376(26): 2507-9.
[http://dx.doi.org/10.1056/NEJMp1702071] [PMID: 28657867]
[77]
Shah N, Srivastava G, Savage DW, Mago V. Assessing canadians health activity and nutritional habits through social media. Front Public Health 2020; 7: 400.
[http://dx.doi.org/10.3389/fpubh.2019.00400] [PMID: 31993412]
[78]
Tsigalou C, Panopoulou M, Papadopoulos C, Karvelas A, Tsairidis D, Anagnostopoulos K. Estimation of low-density lipoprotein cholesterol by machine learning methods. Clin Chim Acta 2021; 517: 108-16.
[http://dx.doi.org/10.1016/j.cca.2021.02.020] [PMID: 33667481]
[79]
Dinh A, Miertschin S, Young A, Mohanty SD. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Med Inform Decis Mak 2019; 19(1): 211.
[http://dx.doi.org/10.1186/s12911-019-0918-5] [PMID: 31694707]
[80]
Çubukçu HC, Topcu DI. Estimation of low-density lipoprotein cholesterol concentration using machine learning. Lab Med 2022; 53(2): 161-71.
[http://dx.doi.org/10.1093/labmed/lmab065] [PMID: 34635916]
[81]
Mandl KD, Manrai AK. Potential excessive testing at scale. JAMA 2019; 321(8): 739-40.
[http://dx.doi.org/10.1001/jama.2019.0286] [PMID: 30735228]
[82]
Lee T, Kim J, Uh Y, Lee H. Deep neural network for estimating low density lipoprotein cholesterol. Clin Chim Acta 2019; 489(35–40): 35-40.
[http://dx.doi.org/10.1016/j.cca.2018.11.022] [PMID: 30448282]
[83]
Singh G, Hussain Y, Xu Z, et al. Comparing a novel machine learning method to the Friedewald formula and Martin-Hopkins equation for low-density lipoprotein estimation. PLoS One 2020; 15(9): e0239934.
[http://dx.doi.org/10.1371/journal.pone.0239934] [PMID: 32997716]
[84]
Lee BJ, Kim JY. Identification of the best anthropometric predictors of serum high- and low-density lipoproteins using machine learning. IEEE J Biomed Health Inform 2015; 19(5): 1747-56.
[http://dx.doi.org/10.1109/JBHI.2014.2350014] [PMID: 25148675]
[85]
Cortes C. Support-vector networks. Mach Learn 1995; 20(3): 273-97.
[http://dx.doi.org/10.1007/BF00994018]
[86]
Awad M, Khanna R. Efficient learning machines: Theories, concepts, and applications for engineers and system designers. Effic Learn Mach Theor Concepts, Appl Eng Syst Des. 2015; 1-248.
[http://dx.doi.org/10.1007/978-1-4302-5990-9]
[87]
Vashistha R, Dangi A K, Kumar A, et al. Futuristic biosensors for cardiac health care: An artificial intelligence approach. 3 Biotech 2018; 8: 1-11.
[http://dx.doi.org/10.1007/S13205-018]
[88]
Aryal S, Alimadadi A, Manandhar I, Joe B, Cheng X. Machine learning strategy for gut microbiome-based diagnostic screening of cardiovascular disease. Hypertension 2020; 76(5): 1555-62.
[http://dx.doi.org/10.1161/HYPERTENSIONAHA.120.15885] [PMID: 32909848]

Rights & Permissions Print Cite
Article Metrics
22
3
Wayfinder Image
© 2024 Bentham Science Publishers | Privacy Policy