Fuzzy Classifier for the Diagnosis of Pathology on the Vertebral Column

This paper describes the training and application of a fuzzy singleton classifier that is used for the diagnosis of pathologies of the vertebral column, discriminating patients as belonging to one out of three categories: Normal, Disk Hernia and Spondylolisthesis. Each input variable is partitioned into triangular membership functions so that consecutive fuzzy sets exhibit and specific overlapping of 0.5. Singleton consequents are employed and least square method is used to adjust the consequents. Applications are conducted on the vertebral column data set from University of California Irvine UCI Machine Learning Repository. Features of the dataset are obtained from sagittal panoramic radiographies of the spine. The dataset includes 310 instances (310 patients) having 6 real valued attributes in 3 classes.