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 Pazok MD PhD FHEA is a medical doctor and an epidemiologist. She studied Epidemiology at Netherlands Institute for Health Sciences (NIHES) and in the University of Amsterdam. She worked with various cohort and case control studies such as the Arrhythmia Genetics in the Netherlands (AGNES), the Rotterdam Study, the Airwave Health Monitoring Study and the UK Bio bank. In 2016, she joined the Department of Epidemiology and Bio-statistics at Imperial College London as a Research Associate. In 2020 she moved to Brunel University London as Lecturer in Biomedical Sciences while maintaining Honorary Lecturer status at Imperial College, London. Dr Pazoki works in the area of cardiovascular and metabolic disorders and health data research. Her research is focused on the large-scale identification of clinical and genetic determinants of cardiovascular and metabolic disorders.   -

Related Research Group(s)

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.