Application of J48 and bagging for classification of vertebral column pathologies

Abstract

Disk hernia and spondylolisthesis are examples of pathologies on vertebral column. These traumas on vertebral column can affect spinal cord capability to send and receive messages from brain to the body systems that control sensor and motor. Therefore, accuracy and timeliness of diagnosis for these pathologies are critical. Hence, a classification system can assist radiologists to improve productivity and the quality of diagnosis. In general, Indonesia's public hospitals have many patients, thus, such classification system will be a great benefit. However, research about pathology of skeletal system classification in Indonesia is rare due to the unavailability of numerical database which quantitatively represents the disease. In this research, dataset of vertebral column from UCI Machine Learning was used to develop an optimum classification model. We ensemble decision tree (J48) and bagging as the classification model. Decision tree was chosen as the base learner due to its simplicity and interpretability. In addition, bagging was used to stable the prediction of new test instances. By applying 10-fold cross-validation we calculated true-positive rate (TP rate), false-positive (FP rate), accuracy parameters, and ROC AUC. The results showed that J48 and Bagging has better performance than J48 alone. The quantitative evaluation showed accuracy of J48 and Bagging is 85.1613%, whereas accuracy of J48 was 81.6129%.