Boosted decision trees for vertebral column disease diagnosis

Abstract

Vertebral column diseases are of the main public health problems which cause a negative impact on patients. Disk hernia and spondylolisthesis are examples of pathologies of the vertebral column which cause intensive pain. Data mining tools play an important role in medical decision making and deal with human short-term memory limitations quite effectively. This paper presents a decision support tool that can help in detection of pathology on the vertebral column using three types of decision trees classifiers. They are Single Decision Tree (SDT), Boosted Decision Tree (BDT), and Decision Tree Forest (DTF). Decision Tree and Regression (DTREG) software package is used for simulation and the database is available from UCI Machine Learning Repository. The performance of the proposed structure is evaluated in terms of accuracy, sensitivity, specificity, ROC curves, and other metrics. The results showed that the accuracies of SDT and BDT in the training phase are 90. 65 and 96. 77%, respectively. BDT performed better than SDT for all performance metrics. Value of ROC for BDT in the training phase is 0. 9952. In the validation phase, BDT achieved 84. 84% accuracy, which is superior to SDT (81. 94%) and DTF (84. 19%). Results showed also that grade of spondylolisthesis is the most relevant feature for classification using BDT classifier.