Statistical machine learning for complex system modelling: diagnosis and prediction of coronary heart diseases

Project title: Statistical machine learning for complex system modelling: diagnosis and prediction of coronary heart diseases

Director of Studies: Dr Mu Niu 

Second Supervisor: Dr Yinghui Wei

Project description

Coronary heart disease is a major cause of death both in the UK and worldwide. The change in the geometry of the heart is clinically termed remodelling, which typically occurs after a heart attack. Non-invasive tomographic imaging of cardiac shape and motion provides information about remodelling. Important diagnostic information can be obtained from the shape, deformation and stress of the heart. However, traditional clinical indices to quantify remodelling are limited to simple measures of mass and volume, discarding much of the available shape information and the dynamics of heart motion. There is a lack of comprehensive data analysis of the available image data to unpack the diagnostic and predictive information about coronary heart diseases. This project aims to make effective use of clinical image data, proposing advanced statistical machine learning methods and producing reliable inference (Niu et al., 2016; Mangion et al., 2017), to develop diagnostic and predictive tools for coronary heart diseases.

 The objectives of this project are listed below:

1.   A non-parametric Bayesian model will be constructed by taking the shape information as inputs and make prediction on the patient states. The important predictors for coronary heart diseases can also be identified. There will be a particular emphasis on Gaussian process and Bayesian inference in this step.

2.   The heart motion can be characterised by a dynamical model. By combining the dynamical model with the non-parametric Bayesian model, we will improve the prediction accuracy of the coronary heart diseases. A novel statistical algorithm will also be developed for the parameter inference of the dynamical model.

3.   The proposed methods will be applied to the existing data collected by our collaborators at University of Glasgow. We will evaluate the validity and the benefits of our proposed approaches over the conventional methods.

We will implement our developed methodology in a user-friendly software toolbox, to promote its uptake by other researchers. We will also develop clinical impact from this project by translating our research output into clinically relevant information to benefit clinicians and patients.

The student will have an excellent training experience in advanced machine learning algorithms and Bayesian statistics through this project, under the guidance of supervisors who have considerable expertise in these areas, in particular Gaussian processes. The student will have good career prospects of working as a data scientist or statistician both within and outside the academic world.

The student will also benefit from this interdisciplinary research. By working with researchers from a variety of field including statistical machine learning, medical statistics and computational biology, the student can review the recent progress in statistical modelling and applications to data science related problems, initiate new collaborations and raise new challenges.


Applicants should have a minimum of a first class or upper second class bachelor degree in mathematics, statistics, computer science, or a related quantitative discipline. Applications from candidates with a relevant masters qualification will be welcomed.


The studentship is supported for three years and includes full home/EU tuition fees plus a stipend of £14,553 per annum. The studentship will only fund those applicants who are eligible for home/EU fees with relevant qualifications. Applicants required to cover overseas fees will have to cover the difference between home/EU and overseas tuition fee rates (approximately £10,350 per annum). General information about applying for a research degree at Plymouth University is available at:

You can apply via the online application form which can be found at: and select ‘Apply’.

Please mark it FAO Mrs Carole Watson and clearly state that you are applying for a PhD studentship within the School of Computing, Electronics and Mathematics.

For more information on the admissions process contact Carole Watson.

Closing date for applications: 12 noon, 6 April 2018.

Shortlisted candidates will be invited for interview in April. We regret that we may not be able to respond to all applications. Applicants who have not received an offer of a place by 4 May 2018 should consider their application has been unsuccessful on this occasion.