  Objective. Epidemiological studies require data that are in alignment with
the classifications established for occupations or economic activities. The
classifications usually include hundreds of codes and titles. Manual coding of
raw data may result in misclassification and be time consuming. The goal was to
develop and test a web-tool, named Procode, for coding of free-texts against
classifications and recoding between different classifications. Methods. Three
text classifiers, i.e. Complement Naive Bayes (CNB), Support Vector Machine
(SVM) and Random Forest Classifier (RFC), were investigated using a k-fold
cross-validation. 30 000 free-texts with manually assigned classification codes
of French classification of occupations (PCS) and French classification of
activities (NAF) were available. For recoding, Procode integrated a workflow
that converts codes of one classification to another according to existing
crosswalks. Since this is a straightforward operation, only the recoding time
was measured. Results. Among the three investigated text classifiers, CNB
resulted in the best performance, where the classifier predicted accurately
57-81% and 63-83% classification codes for PCS and NAF, respectively. SVM lead
to somewhat lower results (by 1-2%), while RFC coded accurately up to 30% of
the data. The coding operation required one minute per 10 000 records, while
the recoding was faster, i.e. 5-10 seconds. Conclusion. The algorithm
integrated in Procode showed satisfactory performance, since the tool had to
assign the right code by choosing between 500-700 different choices. Based on
the results, the authors decided to implement CNB in Procode. In future, if
another classifier shows a superior performance, an update will include the
required modifications.
