title: Effective automated prediction of vertebral column pathologies based on logistic model tree with SMOTE preprocessing.
abstract: This study develops a logistic model tree based automation system based on for accurate recognition of types of vertebral column pathologies. Six biomechanical measures are used for this purpose: pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis. A two-phase classification model is employed in which the first step is preprocessing the data by use of Synthetic Minority Over-sampling Technique (SMOTE), and the second one is feeding the classifier Logistic Model Tree (LMT) with the preprocessed data. We have achieved an accuracy of 89.73 %, and 0.964 Area Under Curve (AUC) in computer based automatic detection of the pathology. This was validated via a 10-fold-cross-validation experiment conducted on clinical records of 310 patients. The study also presents a comparative analysis of the vertebral column data with the use of several machine learning algorithms.
authors.affiliation: Vocational High School of Higher Education, Gaziantep University, 27310, Gaziantep, Turkey.
authors.forename: Esra Mahsereci
authors.initials: EM
authors.lastname: Karabulut
authors.forename: Turgay
authors.initials: T
authors.lastname: Ibrikci
mesh_terms: D019540:Area Under Curve; D001696:Biomechanical Phenomena; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D011859:Radiography; D013122:Spinal Diseases / Q000175:diagnosis*; D013131:Spine / Q000000981:diagnostic imaging* / Q000473:pathology*; D013168:Spondylolisthesis / Q000000981:diagnostic imaging / Q000473:pathology
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