# Using Machine Learning to Predict Cardiovascular Disease for use in a Decision Model

School of Mathematics and Physics | PHD

Applications are now CLOSED
Funding
Funded
Reference Number
MAP/2021/001
28 February 2021
Start Date
1 October 2021

### Overview

There are effective drugs for preventing cardiovascular disease (CVD) for individuals who are at “high risk” of a cardiovascular event. Many individuals at “low risk” prefer to judge for themselves the balance of risks, costs and benefits from taking a medication potentially for life. Machine learning techniques can be used to extract further information to aid prediction of diseases and help classify individuals into more accurate risk groups [1] and should be explored as advanced tools for decision-making [2]. Decision models can help stakeholders such as patients and clinicians by estimating the associated costs and outcomes of taking a drug compared to the standard of care. Discrete Conditional Survival (DC-S) models are a family of models that can represent a skewed survival distribution for different classes of individuals. A set of related variables are used to determine the clustering or grouping of individuals into distinct classes. Previous research has used various conditional components such as decision trees, Naïve Bayes and Bayesian classifiers to classify individuals into discrete classes and represent their survival [3,4]. A number of different survival distributions can be considered and can be represented as a Markov process. Decision models often contain a Markov component that are used to describe the survival time of individuals. A Markov model can be used to describe the process where the risk of an event is continuous over time and the event can happen multiple times [5]. Markov models are useful in healthcare for modelling the progression of a disease of an individual and have been increasingly used within decision models partly due to recent advances in computing which permits higher processing capacities.

This PhD will focus on using different machine learning techniques to predict CVD by classifying individuals into different risk groups and incorporating this into the Markov component of a decision model.

Quantitative training such as mathematics or statistics is required for this project. An interest in the area of medical statistics or data analytics and experience of either (computational and application based) will be considered an advantage.

References

[1] Angermueller, Parnamaa, Parts, Stegle, Deep learning for computational biology. Molecular systems biology, Jul 2016.
[2] Chirikov, Marston, et al., Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps Journal of Health Economics and Outcomes Research, Apr 2020.
[3] Marshall, Burns, Discrete Conditional Survival Models for trolley waiting times in Accident and Emergency. IEEE Workshop on Health Care Management, Feb 2010.
[4] Marshall, Payne et al., Modelling the development of late onset sepsis and length of stay using discrete conditional survival models with a classification tree component. 25th IEEE International Symposium on Computer-Based Medical Systems, June 2012.
[5] Sonnenberg FA, Beck JR, Markov models in medical decision making: a practical guide, Medical decision making, Dec 1993.

Further information:

Supervisory team:
Dr Felicity Lamrock
Professor Frank Kee

#### Funding Information

###### Project Summary
Supervisor
Dr Felicity Lamrock
Mode of Study

Full-time: 3 years

Funding Body
DfE

#### Mathematics overview

The Mathematical Research Centre conducts world-class research in the following areas: Algebra, Analysis, Operator Algebras, Algebraic Topology, Topological Data Analysis, PDEs, Survival Analysis, Bayesian Networks, Data Analytics and Operational Research. It maintains vibrant international links with a large number of researchers around the globe and regularly hosts international conferences and research visitors.

List of researchers, their interests and upcoming PhD projects can be found at:
https://web.am.qub.ac.uk/wp/msrc/.

Mode of study / duration
Registration is on a full-time or part-time basis, under the direction of a supervisory team appointed by the University. You will be expected to submit your thesis at the end of three years of full-time registration for PhD, or two years for MPhil (or part-time equivalent).

##### Mathematics Highlights
• The School has many industry links, some of which are with Seagate Technology R&D, Andor Technology and AVX Ltd. Many of our graduates take up positions with these companies in posts such as Statistical Analysis Programmer, Trainee Accountant, Financial Engineer and Business Analyst.
##### Career Development
• Queen’s is ranked in the top 140 in the world for graduate prospects (QS Graduate Employability Rankings 2020). Graduates from the School take up employment through a number of companies such as Allstate, AquaQ Analytics, Citigroup, Deloitte, PwC, Randox, Seagate and UCAS.
##### World Class Facilities
• Since 2014, the School has invested over £12 million in new world-class student and staff facilities. Maths and Physics students now have their own teaching centre that opened in 2016 housing experimental physics laboratories, two large computer rooms for mathematical simulations and student study plus a student interaction area.
In addition, Northern Ireland has the lowest student cost of living in the UK (Which? University, 2018) being £5000 per year cheaper for students to live in Northern Ireland compared to London (Which? University 2018).
##### Key Facts

• Students will have access to our facilities, resources and our dedicated staff. The School of Maths & Physics is one of the largest Schools in the University. Staff are involved in cutting-edge research that spans a multitude of fields.

#### Course content

##### Research Information

Research Themes
Information on the research interests and activities of academics in the Mathematical Science Research Centre can be found at https://web.am.qub.ac.uk/wp/msrc/. These interests fit into the themes: Algebra, Analysis, Data Science, Optimization and Operational Research, Partial Differential Equations, Statistics, Topology and Geometry.

##### Career Prospects

Introduction
Mathematical and statistical skills are in great demand in the economy, particularly the advanced skills developed at the PhD level.

Employment after the Course
As well as continuing in research careers, our PhD graduates have also gone on to work in finance, computing, data analysis, management and teaching. Our advisors will be happy to provide further information on the career prospects arising from your chosen research area. Further information on careers can be obtained from the School and the Faculty.

##### People teaching you

Dr David Barnes

Email: d.barnes@qub.ac.uk

Dr Salissou Moutari
Director of Research - Mathematical Sciences Research Centre

Email: s.moutari@qub.ac.uk

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##### Assessment

Assessment processes for the Research Degree differ from taught degrees. Students will be expected to present drafts of their work at regular intervals to their supervisor who will provide written and oral feedback; a formal assessment process takes place annually.

This Annual Progress Review requires students to present their work in writing and orally to a panel of academics from within the School. Successful completion of this process will allow students to register for the next academic year.

The final assessment of the doctoral degree is both oral and written. Students will submit their thesis to an internal and external examining team who will review the written thesis before inviting the student to orally defend their work at a Viva Voce.

##### Feedback

Supervisors will offer feedback on draft work at regular intervals throughout the period of registration on the degree.

##### Facilities
Students will enjoy the benefits of modern practical laboratories, extensive computer facilities and interactive spaces.

#### Entrance requirements

The minimum academic requirement for admission to a research degree programme is normally an Upper Second Class Honours degree from a UK or ROI HE provider, or an equivalent qualification acceptable to the University. Further information can be obtained by contacting the School.

##### International Students

For information on international qualification equivalents, please check the specific information for your country.

##### English Language Requirements

Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required. *Taken within the last 2 years.

International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.

For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.

If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.

As a result of the COVID-19 pandemic, we will be offering Academic English and Pre-sessional courses online only from June to September 2020.

• Academic English: an intensive English language and study skills course for successful university study at degree level
• Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.

#### Tuition Fees

 Northern Ireland (NI) 1 £4,500 Republic of Ireland (ROI) 2 £4,500 England, Scotland or Wales (GB) 1 £4,500 EU Other 3 £17,460 International £17,460

1 EU citizens in the EU Settlement Scheme, with settled or pre-settled status, are expected to be charged the NI or GB tuition fee based on where they are ordinarily resident, however this is provisional and subject to the publication of the Northern Ireland Assembly Student Fees Regulations. Students who are ROI nationals resident in GB are expected to be charged the GB fee, however this is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.

2 It is expected that EU students who are ROI nationals resident in ROI will be eligible for NI tuition fees, in line with the Common Travel Agreement arrangements. The tuition fee set out above is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.

3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.

All tuition fees quoted are for the academic year 2021-22, and relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.

Mathematics costs

There are no specific additional course costs associated with this programme.

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