A system that was able to predict disease risk from the genetic data of a patient would have
many valuable applications in medicine, as it would allow for the prevention and early detection of
health conditions. At the moment the effectiveness of disease risk prediction methods is very
limited; this may be because these methods do not use enough of the available genetic data to make
predictions and therefore miss vital information. Machine learning techniques are well suited to
problems involving large amounts of data, and could be incorporated into the process of disease risk
prediction to allow more of the genetic information to be used, potentially improving predictions. If
a machine learning model was able to predict disease risk better than current methods, analysis of
this model could also yield new insights about the genetic basis of disease, informing future
research in this field. This work aims to use machine learning techniques to improve the prediction
of obesity risk from genetic data.
