Application of the Naïve Bayesian Classifier to optimize treatment decisions.

BACKGROUND AND PURPOSE To study the accuracy, specificity and sensitivity of the Naïve Bayesian Classifier (NBC) in the assessment of individual risk of cancer relapse or progression after radiotherapy (RT). MATERIALS AND METHODS Data of 142 brain tumour patients irradiated from 2000 to 2005 were analyzed. Ninety-six attributes related to disease, patient and treatment were chosen. Attributes in binary form consisted of the training set for NBC learning. NBC calculated an individual conditional probability of being assigned to: relapse or progression (1), or no relapse or progression (0) group. Accuracy, attribute selection and quality of classifier were determined by comparison with actual treatment results, leave-one-out and cross validation methods, respectively. Clinical setting test utilized data of 35 patients. Treatment results at classification were unknown and were compared with classification results after 3 months. RESULTS High classification accuracy (84%), specificity (0.87) and sensitivity (0.80) were achieved, both for classifier training and in progressive clinical evaluation. CONCLUSIONS NBC is a useful tool to support the assessment of individual risk of relapse or progression in patients diagnosed with brain tumour undergoing RT postoperatively.