Acute Lymphoblastic Leukaemia (ALL) is a common form of blood cancer, usually affecting
children under 15 years of age. Chemotherapy treatment for ALL is delivered in three phases
viz. induction (to achieve initial remission), intensification (to kill the majority of abnormal
cells), and finally, maintenance. The maintenance phase involves oral administration of the
chemotherapy drug 6-Mercaptopurine (6-MP) in varying doses to destroy any remaining
abnormal cells and prevent reoccurrence. A key side effect of the treatment is a reduction in
neutrophil counts that can result in a condition known as neutropenia, i.e. reduced immune
system. This carries a risk of secondary infection and has been linked to 60% of ALL fatalities.
Current practice aims to control neutrophil counts by varying 6-MP dosages on a weekly basis
based on blood counts. However, its success is varied.
This thesis proposes a number of intelligent prediction methods to more accurately predicting
neutrophil counts one week ahead using blood count data and corresponding 6-MP dosing
regimens. Firstly, a well-known and robust neural network (Nonlinear Autoregressive
Exogenous) is applied to blood count data to provide an initial assessment of the feasibility of
such an approach. A comparative analysis of a series of more complex algorithms is then
considered for more advanced, in-depth analysis viz. Multi-Layer Perceptron (MLP) and
Support Vector Machines (SVM). Both methods are shown to have a prediction accuracy of
around 60% on the first sample period, with the MLP also having a prediction accuracy of more
than 60% in the second sample period in seven out of ten blood data points (there was 10 timeseries blood data predictions). However, in comparison the accuracy of SVM is relatively low.
Finally, an incremental learning-based approach is proposed to increase the accuracy of the
system and provide a realistic framework for real-time implementation. The accuracy is shown
to improve considerably as more data is added, and the predicted neutrophils data is shown to
follow the trend of the actual neutrophil counts
