Resolution:
## Figure 1.
Rule generation workflow. The initial 182 patients dataset is randomly divided into training and test sets.
The training set is used by the supervised learning procedure to iteratively calculate
the LLM parameter:" maximum error allowed for a rule" by performing a complete 10-fold
cross validation. The whole training set is randomly subdivided into 10 non-overlapping
subsets, nine of which are used to train the classifier by employing ADID and LLM.
The classifier is subsequently used to predict the outcome of the patients in the
excluded subset. This procedure is repeated 10 times until every subset is classified
once. Each parameter value is then evaluated according to the mean classification
accuracy obtained in the cross validation. The parameter value, which obtained the
highest mean accuracy, is selected to generate the final optimal classification rules.
The rules are then tested on an independent cohort to assess their ability to predict
patients' outcome.
Cangelosi |