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Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases / GARETH JONES

Swansea University Author: GARETH JONES

DOI (Published version): 10.23889/SUthesis.58404

Abstract

Two of the most common forms of arterial disease are stenosis and aneurysm, estimated to affect between 1% and 20% of the population. Ruptured abdominal aortic aneurysms alone are estimated to be the cause of between 6,000 and 8,000 deaths a year within the United Kingdom. Patients with stenosis have...

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Published: Swansea 2021
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Pant, S., ; Nithiarasu, P. ; Parr, J.
URI: https://cronfa.swan.ac.uk/Record/cronfa58404
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2021-10-19T14:22:17.0490517</datestamp><bib-version>v2</bib-version><id>58404</id><entry>2021-10-19</entry><title>Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases</title><swanseaauthors><author><sid>fb2a36e7caa997552cc56fde2ea8783a</sid><firstname>GARETH</firstname><surname>JONES</surname><name>GARETH JONES</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-10-19</date><abstract>Two of the most common forms of arterial disease are stenosis and aneurysm, estimated to a&#xFB00;ect between 1% and 20% of the population. Ruptured abdominal aortic aneurysms alone are estimated to be the cause of between 6,000 and 8,000 deaths a year within the United Kingdom. Patients with stenosis have been shown to have a mortality hazard ratio of 1.42 compared to a control population [2], and an unadjusted death rate of 3.35 per 100 person-years compared to 1.23 per 100 person-years in a control population [97]. Current methods for the detection of arterial disease are generally impractical for large scale screening, expensive, or both. If an inexpensive method for the detection of both stenosis and aneurysm is created, that minimises the need for invasive measurements, the cost e&#xFB00;ectiveness of large scale screening could be improved making both continuous monitoring and screening feasible. One such method is to use easily acquirable haemodynamic measurements at accessible peripheral locations within the circulatory system for diagnosis. Within this thesis an initial exploratory study into the potential of using machine learning classi&#xFB01;cation algorithms to detect arterial disease from such measurements is presented.It is likely that the indicative biomarkers of arterial disease held within pressure and &#xFB02;ow-rate pro&#xFB01;les consist of micro inter- and intra- measurement details. To facilitate the use of a data driven approach to the discovery of any biomarkers a framework for the creation of virtual patients, through the employment of a mathematical model of blood &#xFB02;ow, is presented. This framework is utilised to create a series of virtual patient databases, as the balance between simplicity and realism progresses through the thesis. The most realistic of these databases is made publicly available (https://doi.org/10.5281/zenodo.4549764). The aforementioned framework for the creation of virtual patients is a major contribution of this thesis, and can be applied to a wide range of biological systems given a mathematical description.The synthetic data sets are used to train and subsequently test a series of machine learning classi&#xFB01;ers, to predict the presence of both stenosis and aneurysm, using various combinations of pressure and &#xFB02;ow-rate measurements. It is shown that the inclusion of a diseased vessel (either stenosis or aneurysm) produces consistent and signi&#xFB01;cant biomarkers in haemodynamic pro&#xFB01;les, irrespective of a patients unique underlying arterial network. These biomarkers are found to be di&#xFB00;erentiable from the natural variability present across a large cohort of patients, showing that arterial disease has a clear and unique e&#xFB00;ect on pressure and &#xFB02;ow-rate pro&#xFB01;les. This suggests strong potential in the use of haemodynamic measurements to detect arterial disease.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Arterial disease diagnosis, machine learning, virtual patient database, pulse wave haemodynamics</keywords><publishedDay>19</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-10-19</publishedDate><doi>10.23889/SUthesis.58404</doi><url/><notes>ORCiD identifier: https://orcid.org/0000-0002-3196-6663</notes><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Pant, S., ; Nithiarasu, P. ; Parr, J.</supervisor><degreelevel>Doctoral</degreelevel><degreename>Ph.D</degreename><degreesponsorsfunders>EPSRC doctoral training grant</degreesponsorsfunders><apcterm/><lastEdited>2021-10-19T14:22:17.0490517</lastEdited><Created>2021-10-19T12:31:31.4095802</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>GARETH</firstname><surname>JONES</surname><order>1</order></author></authors><documents><document><filename>58404__21223__5fc7c9c34f284d35927c306e8d42c4be.pdf</filename><originalFilename>Jones_Gareth_PhD_Thesis_Final_Redacted_Signature.pdf</originalFilename><uploaded>2021-10-19T14:16:08.6616756</uploaded><type>Output</type><contentLength>24322139</contentLength><contentType>application/pdf</contentType><version>E-Thesis &#x2013; open access</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright: The author, Gareth Jones, 2021.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2021-10-19T14:22:17.0490517 v2 58404 2021-10-19 Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases fb2a36e7caa997552cc56fde2ea8783a GARETH JONES GARETH JONES true false 2021-10-19 Two of the most common forms of arterial disease are stenosis and aneurysm, estimated to affect between 1% and 20% of the population. Ruptured abdominal aortic aneurysms alone are estimated to be the cause of between 6,000 and 8,000 deaths a year within the United Kingdom. Patients with stenosis have been shown to have a mortality hazard ratio of 1.42 compared to a control population [2], and an unadjusted death rate of 3.35 per 100 person-years compared to 1.23 per 100 person-years in a control population [97]. Current methods for the detection of arterial disease are generally impractical for large scale screening, expensive, or both. If an inexpensive method for the detection of both stenosis and aneurysm is created, that minimises the need for invasive measurements, the cost effectiveness of large scale screening could be improved making both continuous monitoring and screening feasible. One such method is to use easily acquirable haemodynamic measurements at accessible peripheral locations within the circulatory system for diagnosis. Within this thesis an initial exploratory study into the potential of using machine learning classification algorithms to detect arterial disease from such measurements is presented.It is likely that the indicative biomarkers of arterial disease held within pressure and flow-rate profiles consist of micro inter- and intra- measurement details. To facilitate the use of a data driven approach to the discovery of any biomarkers a framework for the creation of virtual patients, through the employment of a mathematical model of blood flow, is presented. This framework is utilised to create a series of virtual patient databases, as the balance between simplicity and realism progresses through the thesis. The most realistic of these databases is made publicly available (https://doi.org/10.5281/zenodo.4549764). The aforementioned framework for the creation of virtual patients is a major contribution of this thesis, and can be applied to a wide range of biological systems given a mathematical description.The synthetic data sets are used to train and subsequently test a series of machine learning classifiers, to predict the presence of both stenosis and aneurysm, using various combinations of pressure and flow-rate measurements. It is shown that the inclusion of a diseased vessel (either stenosis or aneurysm) produces consistent and significant biomarkers in haemodynamic profiles, irrespective of a patients unique underlying arterial network. These biomarkers are found to be differentiable from the natural variability present across a large cohort of patients, showing that arterial disease has a clear and unique effect on pressure and flow-rate profiles. This suggests strong potential in the use of haemodynamic measurements to detect arterial disease. E-Thesis Swansea Arterial disease diagnosis, machine learning, virtual patient database, pulse wave haemodynamics 19 10 2021 2021-10-19 10.23889/SUthesis.58404 ORCiD identifier: https://orcid.org/0000-0002-3196-6663 COLLEGE NANME COLLEGE CODE Swansea University Pant, S., ; Nithiarasu, P. ; Parr, J. Doctoral Ph.D EPSRC doctoral training grant 2021-10-19T14:22:17.0490517 2021-10-19T12:31:31.4095802 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised GARETH JONES 1 58404__21223__5fc7c9c34f284d35927c306e8d42c4be.pdf Jones_Gareth_PhD_Thesis_Final_Redacted_Signature.pdf 2021-10-19T14:16:08.6616756 Output 24322139 application/pdf E-Thesis – open access true Copyright: The author, Gareth Jones, 2021. true eng
title Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases
spellingShingle Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases
GARETH JONES
title_short Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases
title_full Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases
title_fullStr Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases
title_full_unstemmed Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases
title_sort Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases
author_id_str_mv fb2a36e7caa997552cc56fde2ea8783a
author_id_fullname_str_mv fb2a36e7caa997552cc56fde2ea8783a_***_GARETH JONES
author GARETH JONES
author2 GARETH JONES
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college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
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description Two of the most common forms of arterial disease are stenosis and aneurysm, estimated to affect between 1% and 20% of the population. Ruptured abdominal aortic aneurysms alone are estimated to be the cause of between 6,000 and 8,000 deaths a year within the United Kingdom. Patients with stenosis have been shown to have a mortality hazard ratio of 1.42 compared to a control population [2], and an unadjusted death rate of 3.35 per 100 person-years compared to 1.23 per 100 person-years in a control population [97]. Current methods for the detection of arterial disease are generally impractical for large scale screening, expensive, or both. If an inexpensive method for the detection of both stenosis and aneurysm is created, that minimises the need for invasive measurements, the cost effectiveness of large scale screening could be improved making both continuous monitoring and screening feasible. One such method is to use easily acquirable haemodynamic measurements at accessible peripheral locations within the circulatory system for diagnosis. Within this thesis an initial exploratory study into the potential of using machine learning classification algorithms to detect arterial disease from such measurements is presented.It is likely that the indicative biomarkers of arterial disease held within pressure and flow-rate profiles consist of micro inter- and intra- measurement details. To facilitate the use of a data driven approach to the discovery of any biomarkers a framework for the creation of virtual patients, through the employment of a mathematical model of blood flow, is presented. This framework is utilised to create a series of virtual patient databases, as the balance between simplicity and realism progresses through the thesis. The most realistic of these databases is made publicly available (https://doi.org/10.5281/zenodo.4549764). The aforementioned framework for the creation of virtual patients is a major contribution of this thesis, and can be applied to a wide range of biological systems given a mathematical description.The synthetic data sets are used to train and subsequently test a series of machine learning classifiers, to predict the presence of both stenosis and aneurysm, using various combinations of pressure and flow-rate measurements. It is shown that the inclusion of a diseased vessel (either stenosis or aneurysm) produces consistent and significant biomarkers in haemodynamic profiles, irrespective of a patients unique underlying arterial network. These biomarkers are found to be differentiable from the natural variability present across a large cohort of patients, showing that arterial disease has a clear and unique effect on pressure and flow-rate profiles. This suggests strong potential in the use of haemodynamic measurements to detect arterial disease.
published_date 2021-10-19T04:14:54Z
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score 11.000887

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