Abstract
The development of prognostic models for assisting medical practitioners with decision
making is not a trivial task. Models need to possess a number of desirable characteristics
and few, if any, current modelling approaches based on statistical or artificial intelligence
can produce models that display all these characteristics. The inability of modelling
techniques to provide truly useful models has led to interest in these models being
purely academic in nature. This in turn has resulted in only a very small percentage
of models that have been developed being deployed in practice. On the other hand,
new modelling paradigms are being proposed continuously within the machine learning
and statistical community and claims, often based on inadequate evaluation, being
made on their superiority over traditional modelling methods. We believe that for
new modelling approaches to deliver true net benefits over traditional techniques,
an evaluation centric approach to their development is essential. In this paper we
present such an evaluation centric approach to developing extensions to the basic
k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance
the distance metric used and a framework based on evidence theory to obtain a prediction
for the target example from the outcome of the retrieved exemplars. We refer to this
new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing
a means for handling censored observations within k-NN.
Keywords
Colorectal Cancer - Survival Analysis - Nearest Neighbour - Censored Observations
