Wednesday 15th August

I'm making this decision as I type it into TMA03. I mentioned on the blog before that I wanted to compare like with like so I wanted to construct a neural network with the same number of inputs as the flex program. So for a rule inthe flex program such as:

uncertainty_rule r4c

if the involved_nodes is '>=6 and <=17'

then the prognosis is reccurence

with certainty factor 0.20 .

The equivalent input in the neural network will be given by whether the statement:

Involved node is greater than or equal to 6 but less than or equal to 17

is true or false.

Monday 13th August - Oops

Noted when writting up the draft for TMA03 that I have not mentioned that I decided to use Positive Predictive Value, Specifity and Sensitivity as performance indicators as these are more universally understood than the OU score tool.

Monday 13/08/07

Spent most of yesterday writing Q2 of TMA03. I'm not sure I entirely understand what is menat by the "doing" part of the project but I've done what I can, bearing in mind the practical work is far from repeat.

I've actually started to enjoy using Flex. I've been modifying the program that uses certainty factors and added more rules (kbs 6 and 7) and have tested with a couple of patients and it seems to work fine. The certainties of the evidence which are used at the start of the program have caused much head scratching. For example:

All patients under 30 years old do not suffer recurrence. So I can asign a certainty of -1.0 to the rule for the certainty that the patient will suffer recurrence. But if the patient is over 30 the evidence then the statement "is the patient under 30" is definitely not true so the certainty factor in the starting statement entered in the console would be -1. Implying that recurrence must occur which isn't true, I think.

So I've fiddled with the certainty factors in the rules to try to overcome this problem and tested with -1 or 0 in the starting statement when the condition is not true.

I am actually finding the TMA a pain because I actually have some enthusiasm for Flex at the moment and want to get on, but I have to finish the TMA.

Saturday 11th August

Oh well so much for the new season. Some things don't change. Found a useful, if rather old reference for prognostic indicators in breast cancer.

Histopathology

Volume 19 Issue 5 Page 403-410, November 1991

To cite this article: C.W. ELSTON, I.O. ELLIS (1991)
pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up
Histopathology 19 (5), 403–410.

Need to look for it at work.

Friday 11th August

Modified flex certainty factor program. Program compiles and runs but the results are not as expected. The certainty factor associated with the prognosis is adjusted after each rule so that the certainty factor is the sum after all the rules are evaluated but if all patients under 30 do not have recurrence it doesn't matter what the values the rules which look at location, no of LNs etc assign the certainty that will always be 1 however the other rules are affecting this value. Not easy to explain.

Will read through Hopgood and the example in block one of T396. I think I will need less rules with or staements in them.

TMA really needs to get moving tomorrow. Not sure how to tackle it. Too late to contact tutor now. Will do the best I can in the time I have.

Must state that all I want is to pass this project. After 7 years of OU I am utterly fed up with studying.

Football season kicks of tomorrow. The only time I will be away from this PC will be to watch the Hammers.

Wednesday 8th August

TMA deadline looming. Need to get it sorted.

Spent some time modifying my flex program using certainty factors. It is almost working. Hopefully a little more time will sort it out. I will see if I can add to the 6 rules I have. If not I will test when it is working. Need to sort out testing and scoring strategy. Probably use a similar score tool as the NN but with less instances to test. Upto 50 patients would be enough.

Then I'll modify the NN so that it has the same data inputs as the flex program and test. That way I'll be testing like with like.

Probably resume this work now after the TMA.

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.