Using Hyperdimensional Computing to Extract Features for the Detection of Type 2 Diabetes

Diabetes impacts around 8% of the world's population, with Type 2 diabetes comprising up to 90% of cases. This chronic disease is characterized by a metabolic resistance to insulin which results in a high blood sugar level and increased potential for serious health complications. Preventative medicine and the detection of genetic predisposition play a key part in successful treatment. Although several factors have been identified as possible indicators of underlying diabetes, they are not the same in every patient. There have been different approaches to producing predictive models that could help identify risk of onset diabetes. Models built using Machine Learning algorithms have showed promise in the past in detecting relevant features in sample datasets with data from patients at risk of developing diabetes. However, overall performance has not been consistent across datasets. In this paper we describe a feature extraction approach using Hyperdimensional Computing as a tool for improving already existing classification models. We tested our approach using two public datasets and compare across several state of the art models. Our approach improves poor performing models while fine tuning models with a high classification accuracy.