Experiment 1: Classifiers
Following the dataset preprocessing, the next step in implementing RECTIN is classification module development. The classification module will use a model built on historical patients’ data, in order to support physicians in suggesting optimal treatment approach for new patients. Categorization is rather easy and relatively broad. However, a specific approach within each category varies. Before implementing this module, it is necessary to extract new, useful features and conduct experiments in order to obtain the most accurate classifier on the prepared dataset. It is assumed to reiterate the step of feature development in order to obtain the best combination of feature extraction/selection method and the prediction method. This involves the calibration and tuning of prediction methods, as well as comparing them and evaluating in terms of accuracy, F-score and confusion matrix.