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Effective Automated Prediction of Vertebral Column Pathologies Based on Logistic Model Tree with SMOTE Preprocessing

  • Systems-Level Quality Improvement
  • Published:
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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.

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Authors and Affiliations

  1. Vocational High School of Higher Education, Gaziantep University, 27310, Gaziantep, Turkey

    Esra Mahsereci Karabulut

  2. Department of Electrical-Electronics Engineering, Çukurova University, 01330, Adana, Turkey

    Turgay Ibrikci

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  1. Esra Mahsereci Karabulut
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  2. Turgay Ibrikci
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Correspondence to Turgay Ibrikci.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Karabulut, E.M., Ibrikci, T. Effective Automated Prediction of Vertebral Column Pathologies Based on Logistic Model Tree with SMOTE Preprocessing. J Med Syst 38, 50 (2014). https://doi.org/10.1007/s10916-014-0050-0

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  • DOI: https://doi.org/10.1007/s10916-014-0050-0

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