Saturday 19th May

May have been a bit hasty declaring I had preprocessed all the data. I have been looking at the statistical analysis of the data today and have identified an number of rules. After having read through the T396 project I have decided to handle uncertainty using certainty factors as I seem to ba able to understand this more than fuzzy logic and baysian updating. After the statistical analysis I have decided that I will not use all the possible attributes as input neurons for the neural network.

For example there seems to be no significance between the location of the tumour in the breast and the chances of reccurence of the tumour, apart from a much lower incidence of reccurence if the tumour is located centrally. Therefore I will cut this down from 5 input neurons (one for each quadrant) to 2 input neurons (central or not). There are several other possible situations where the number of input neurons could be reduced.

I hope to very shortly complete the rules identification. I have four rules that are always true and the rest are mostly true so will need to be used with the certainty factors.

As an aside and worth remembering when discussing this project is that there is no indication of how long was it from when the tumour was removed to the assessment of whether there was reccurence or not, i.e. if the patients without reccurence were followed up a few years later would the tumour still not have reoccured.

16th May

Did some statistical analysis on the breast data in a similar way to the T396 project I did in 2005. I am trying to look for some rules in the data but they are not obvious. Looked at the examples on the course for using certainty factors in flex. Still not sure which way to do this, uncertainty factors, fuzzy logic or baysian updating.

Am spending so much time configuring the order comms system at work that I am less than keen to spend time using a computer when I get home. Still the weather's crap which helps.

15th May

Football season over. West Ham safe, now to pull my finger out again. Finished preprocessing the data for the neural network.

Wednesday 25th April

Started preprocessing data by converting two data fields (side and sector) into one (quadrant). Bit of a pain updating 285 records. Still it's a start.

Saturday 21/04/07

Spent some time analysing the data for trends to develop rules. There are some that stand out e.g. the higher the histological grade the greater the chances of recurrence. This is out all I've done lately but I'm not worried as I there's a some room in the schedule at the moment.

It's been a really good day as I recieved my TMA result and was shocked at how good it was. I would have been happy with 50%. Anyway I mustn't let it go to my head. West Ham won too so great.

Tuesday 3rd April

Completing TMA01. Have drawn up a schedule for the project because I had to for question 1. I should have done this earlier that way I may not have spent so much time chosing the dataset and may have made more progress in deciding what I would actually do. Hence I have been rather vague about the KBS because I should have spent more time on this.

Added link for background info about breast cancer prognosis

Week beginning 25th March

Spent time at work extracting breast cancer data from WinPath. I spoke to staff from the breast care team to see if I can find out about patient outcomes from those who had histology results. I have a couple of contacts to follow up.

I have decided to try to use the data from work if I can get the clinical outcomes because I always have the breast cancer data that I have down loaded as a backup if this isn't achieveable. I will have to be careful not to use patient identifiable data because of the Data Protection Act. Therefore I won't be able to use a date of birth field but age will be OK.

Downloaded several papers on breast cancer prognosis and artificial intelligence via IEEE Xplore. I am reading through these but generally they have used neural networks with some success and fuzzy logic in hybrid systems. I would like to use neural networks in a similar way although, providing I can use the data from work, I will use different prognostic indicators such as oestrogen and progesterone receptor status and her2 status which were not included in these papers. I will try training a multilayer perceptron and comparing this with a radial basis function network and/or Kohonen self organising network to look at supervised vs unsupervised learning. I will try using an one of these network that use unsupervised learning to cluster the data coupled to a MLP to classify the data clusters.

I will try using a rule based system to compare it's performance with that of the neural networks. I will try using fuzzy logic to produce rules based on analysing the data I have. For example: If number of lymph nodes is high and the tumour is large then the chances of reoccurence if high.

Now to sort out that TMA.