Comparative Study of Pattern Recognition Techniques in the Classification of Vertebral Column Diseases

This work compares popular classifiers based on pattern recognition techniques of supervised learning, including k-Nearest Neighbors, Naïve Bayes, Support Vector Machines, Artificial Neural Networks, and Decision Trees. Such techniques are applied to a dataset related to vertebral column orthopedic diseases. Different parameter values employed by each classifier are tested, resulting in an accuracy of around 80 $$\%$$ in almost all approaches, where the k-Nearest Neighbors alternatives were the most accurate. Finally, a brief discussion of particular highlights of how the metrics affect the performances of the classifiers is presented.