Postgraduate research

Predicting diabetes-related complications with machine learning techniques

Qualification: PhD

Department: Population Health Sciences

Application deadline: 10 December 2022

Start date: 9 January 2023



Project description:

Diabetes mellitus is characterised by chronic hyperglycaemia associated with a higher risk of cardiovascular complications. Regular monitoring and management of risk factors such as Glycaemic control, blood pressure and lipids and maintaining it within the recommended range is critical to control the disease progression. This constant monitoring generates a large amount of intra-individual longitudinal observations of blood glucose levels: this information can be used to predict diabetes-related multiple complications. 

Recently, the rapid development of machine learning methods has resulted in their applications in various areas of healthcare-related research. This PhD project aims to apply different statistical models and machine learning algorithms (including k-nearest neighbour, classification and regression trees, and supervised principal component analysis) to predict various diabetes-related complications, with the aim to develop tools to create a personalized decision system. The post holder will undertake different statistical analyses using the Clinical Practice Research Datalink (CPRD) database, which includes anonymized patient data from a network of GP practices across England, to identify key features (i.e., age, gender, ethnicity, and diabetes duration), which contribute to the risk of diabetes complications.

The student will be embedded within a team of experts in clinical diabetes, epidemiology, and statistics, and receive training in a broad range of statistical methods used to investigate cross-sectional and longitudinal real-world data, as well as methods for prognostic research (development and validation of predictive models) using machine learning and statistical modelling approaches.

The Ph.D. project will be integrated into a vibrant postgraduate research community within the Real-World Evidence Unit and the Diabetes Research Centre, University of Leicester, and help advance the aims of the National Institute of Health and Care Research Leicester Biomedical Research Centre (BRC) and East Midlands Collaboration for Leadership in Applied Health Research and Care (ARC). 


Zhang, L., Shang, X., Sreedharan, S., Yan, X., Liu, J., Keel, S., ... & He, M. (2020). Predicting the development of type 2 diabetes in a large Australian cohort using machine-learning techniques: longitudinal survey study. JMIR medical informatics, 8(7), e16850.

Lai, H., Huang, H., Keshavjee, K., Guergachi, A., & Gao, X. (2019). Predictive models for diabetes mellitus using machine learning techniques. BMC endocrine disorders, 19(1), 1-9.

Gray, L. J., & Khunti, K. (2013). Type 2 diabetes risk prediction—Do biomarkers increase detection? Diabetes research and clinical practice, 101(3), 245-247. 

Abbasi, A., Peelen, L. M., Corpeleijn, E., Van Der Schouw, Y. T., Stolk, R. P., Spijkerman, A. M., ... & Beulens, J. W. (2012). Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. Bmj, 345.

Collins, G. S., Mallett, S., Omar, O., & Yu, L. M. (2011). Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC medicine, 9(1), 1-14.



This 3-year PhD Studentship provides:

  • UK tuition fee wiaver
  • Annual stipend at standard UKRI rates (£17,668 for 2022/23)

Entry requirements

Entry requirements

Applicants are required to hold a UK Bachelor Degree 2:1 (or overseas equivalent) or better and a Master’s degree in Statistics, Biostatistics, Data Science, Machine learning or Epidemiology.

The University of Leicester English language requirements apply where applicable.

Informal enquiries

Informal enquiries

Project enquiries:

Application enquiries:

How to apply

How to apply

To submit your application, please use the 'Apply' button at the bottom of the page and select January 2023 from the dropdown menu.

Include with your application:

  • CV

  • Personal statement explaining your interest in the project, your experience and why we should consider you

  • Degree Certificates and Transcripts of study already completed and if possible transcript to date of study currently being undertaken

  • Evidence of English language proficiency if applicable

  • In the reference section please enter the contact details of your two academic referees in the boxes provided or upload letters of reference if already available.

  • In the funding section please specify that you wish to be considered for the Diabetes Bhattacharjee Studentship

  • In the proposal section please provide the name of the project supervisors and the project title (a research proposal is not required)





Open to UK (Home) applicants only.

*Applicants holding EU Settled and Pre-Settled status, we will require a UK government share code so that we can verify your status (The share code we require starts with S)  please email your share code together with your application ID to  once you have submitted your PhD application.

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