Later Sunday

Too hot to be really productive. However question 1 of TMA03 is OK. Not entirely sure about question 2.

Decided just to run the flex program with uncertainty rules only as I am sure I can get it to work, though it is cumbersome to test.

Noticed in flex manual that it states:

Given a rule:
rule1: if A & B then C
there are 3 potential areas for uncertainty.
- Uncertainty in data (how true are A and B)
- Uncertainty in the rule (how often does A and B imply C)
- Impreciseness in general
The first 2 can be handled using probabilities and the third using fuzzy logic.

As the main sources of uncertainty in this project is in the data - difficult to measure tumours accurately etc, and the rules, there are only four rules that are always true according to the statistical analysis of this dataset but these are probably not always true with other datasets. Therefore I was justified in using uncertainty rules to deal with these sources of uncertainty.

Sunday 5th August

Back from holiday. I did think I might be able to work on the laptop while away but never did. So am going to work some more on the project draft for TMA03. I have decided that I must compare like with like. In the project I did for T396 there were many more inputs and bird instances used in the neural network than the KBS. Therefore it was hardly surprising it out performed the KBS. After I have a satisfactory NN and KBS working I will either scale up the KBS to use the same number of patient instances as the NN and data inputs, or more likely I will scale down the NN to use the same number of patient instances as the KBS etc.

Back to work.

Saturday 21st July

Forgot to note that I have been working on a flex program for predicting breast cancer recurrence. I am trying to combine an earlier working flex program which had rules which were always true with rules that are not always true. I am using certainty factors to calculate the probability that the cancer will recurr.

I decided to use certainty factors mainly because I feel I can understand them and also because after reading Hopgood discovering they are for uncertain data and rules, not uncertain language.

Anyway off on holiday now. Will have to resume when I get back. I am feeling much more confident that I can get a reasonable flex program working.

Also started a draft of the project which is required for TMA03

Sunday afternoon

Tried to work out how the dialog editor works but there is no mention of it in the help files. It would have been good to enter the patient data using a custom form but I haven't the time to waste trying to sort this out.

Had a read through a tutorial with flex which explained that putting the numbers in between ' would treat them as atoms not as numbers. I have adjusted the code and the program now runs fine.

group no_involved_nodes '0-2', '3-5', '6-8', '18+'.

question involved_nodes
how many involved_nodes are there ? ;
choose from no_involved_nodes.

Sunday 15th July

I thought I'd posted something during the week. I have been working through a series of experiments with a multi-layer perceptron. I have been looking at the number of hidden layer neurons, the number of training cycles and the score tool threshold. I have noticed that the networks performance never gives a score of more than 48% no matter how many training cycles are defined.

I have switched my attention to the knowledge based system. I have been having trouble with Flex because I was using questions with the answers in groups but the answers were numbers and I couldn't get flex to treat them as just text for the purposes of these questions. I have therefore developed questions in a different way. I will test this shortly.

Monday 09/07/2007

After a fustrating day yesterday because Window Vista doesn't seem to let neuralworks write the results files out, I reverted to using Windows XP on a different PC. I tested a 32 Input layer, 15 hidden layer and 2 output layer MLP with the full 285 patients after training the network. I then adjusted the score tool threshold to find an optimum but this wasn't as simple as I thought. As the threshold was raised generally the network performed more poorly and this was reflected in the statistical analysis and the overall score. However when the threshold reached 0.9 though by most measures of performance the network performed more poorly the positive predictive value of the network improved. I checked the calculation of this value but it looks correct so I can only assume that it is probably a statistical anomaly. I may test the score tool threshold further with different network configurations but

Sunday 1st July

Picked up last TMA result. I did about as well as I expected. Have been experimenting with my neural network. Have made up a large spreadsheet to log the results. I have enhanced the score tool to calculate the false positive and false negative rate. I hope to modify it further to include the positive predictive value and the sensitivity as these are well defined measures of the performance of the neural network and allow comparison with other methods of classifying data such as that used here. I've run a few experiments and the network is working though at this stage not very well.

Three weeks till hols so not much time.