Handling outliers and missing data in brain tumour clinical assessment using t-GTM.
Alfredo Vellido, Paulo Lisböa, Dolores Vicente · The European Symposium on Artificial Neural Networks · 2005
Uncertainty is inherent to medical decision making, and automated decision support systems should aim to reduce it. In this paper, MR spectral data are considered in a problem of discrimination of brain tumour types and grades. Models to fit these data can be affected by two sources of uncertainty that might occur in the data: the presence of outliers and data incompleteness. A model for multivariate data clustering and visualization, the GTM, is here redefined as a mixture of Student t-distributions that is robust towards outliers while providing missing values imputation. The effectiveness of this model on the MRS data is demonstrated empirically.