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Machine learning approaches in health data science for risk prediction of cardiovascular diseases

Cardiovascular disease is killing over 17 million people in the world. almost a third of the death from cardiovascular diseases occur suddenly without prior signs and symptoms or diagnosis of any heart conditions. Prediction and prevention of cardiovascular diseases are then a priority to prevent these deaths.

Machine learning approaches are increasingly used in order to improve the prediction of diseases. In this PhD project, you will use machine learning approaches to predict cardiovascular outcomes such as cardiovascular diseases (e.g. heart attack, stroke) or their risk factors (e.g. obesity, diabetes).

We have access to large-scale databases such as the UK Biobank which includes ~500,000 individuals and this gives us a unique opportunity to be able to produce high quality research and publish in high impact journals.

The projects involve working with human data and learning various skills in data science. applicants with a degree (1:1 or 2:1) in the fields related to data analysis such as Data Science, Genomics, Epidemiology, Population Genetics, Statistics, and Bioinformatics or related disciplines and applicants who are experienced working with statistical software such as SPSS, SAS, STATA, or R are encouraged to apply.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)


Raha Pazoki - Raha Pazoki is a Lecturer in Cardiovascular and Metabolic disorders and MRC Rutherford Fund Fellow at Brunel University and Honorary Lecturer at Imperial College, London. She obtained her PhD from University of Amsterdam in 2015 and spent 3 years as Research Associate and Research Fellow at Imperial College, London. She works in the field of health data research. Her research is focused on the large-scale identification of clinical and genetic determinants for complex diseases in relation to behaviour and circulating biomarkers with a focus on cardiovascular diseases. Dr Pazoki is in collaboration with scientists at Imperial College, London, Cambridge University, Erasmus University, Rotterdam and Groningen University in the Netherlands, Million Veteran Programme in the US, and University of Bern in Switzerlands. ============================================================ - PhD projects open to applicants: - If you are a MSc graduates (with upper second class degree or higher) in the fields of Epidemiology, Biostatistics, Bioinformatics, Data Science and are interested in a PhD project in cardiovascualr and metabolic research, please contact Dr Raha Pazoki (raha.pazoki@brunel.ac.uk). Examples of Dr Pazoki's avaialble projects: Genetic and non-genetic factors involved in risk of cardiovascular diseases Interplay between genetic and environmental factors and risk of cardiovascular disease Identification of determinants of sudden cardiac death using population studies Interplay between lifestyle factors and genetic factors modulating coagulation in risk of myocardial infarction Health data analysis to identify the role of gene and environment in risk of cardiovascular diseases Lifestyle factors, fibrinogen, genetics and risk of myocardial infarction If you have any queries regarding these projects or topics in health data research, please contact the primary PhD supervisor Dr Raha Pazoki. -   -

Related Research Group(s)

Cardiovascular and Metabolic Research Group

Cardiovascular and Metabolic Research Group - Understanding the biological, social, physiological aspects of cardiovascular and metabolic diseases and producing knowledge to improve cardiovascular and metabolic health.