Application of Machine Learning Methods for Disease Prediction in High Risk Groups
Genetic variation within the major histocompatibility complex (MHC) is associated with susceptibility to many autoimmune diseases. However, the genes within this region are highly polymorphic with extensive linkage disequilibrium (LD) making feature selection using statistical models very challenging. Machine learning methods are particularly suited to this challenge where feature selection approaches try to find a subset of the original variables that enable more accurate prediction by the elimination of irrelevant and confusing information. Accurate feature selection is essential for predicting disease in high risk groups. For example, approximately 30% of patients with psoriasis develop an inflammatory arthritis referred to as psoriatic arthritis (PsA). Accurate prediction of psoriasis at high risk of developing PsA would allow early intervention and limit the impact on a patient’s quality of life. We focus on the filter feature selection methods based on information theoretic criteria that are classifier independent methods that provide the ranking of genetic features for differentiating PsA from psoriasis. The ensembles predictive models were trained and evaluated ‘with feature selection’ and ‘without feature selection’.
Farideh is a postdoctoral research associate and teaching associate fellow in arthritis research UK centre for genetics and genomics school of biological sciences faculty of biology, medicine and health at Manchester University. Her work aims to bring insight from the field of machine learning to solve clinical problems that can provide clinicians with actionable information. Before joining the arthritis research centre, Farideh applied machine learning techniques to develop an objective patient’s voice-assessment model that is now being used in hospitals. After completing her MSc in microelectronics engineering with distinction level at Newcastle University she was awarded PhD studentship from the school of computer science at Manchester University. During her PhD, she was awarded people’s choice at the University of Manchester for her three minute thesis presentation.