J Med Syst - Effective automated prediction of vertebral column pathologies based on logistic model tree with SMOTE preprocessing.

Tópicos

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Resumo

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.

Resumo Limpo

studi develop logist model tree base autom system base accur recognit type vertebr column patholog six biomechan measur use purpos pelvic incid pelvic tilt lumbar lordosi angl sacral slope pelvic radius grade spondylolisthesi twophas classif model employ first step preprocess data use synthet minor oversampl techniqu smote second one feed classifi logist model tree lmt preprocess data achiev accuraci area curv auc comput base automat detect patholog valid via foldcrossvalid experi conduct clinic record patient studi also present compar analysi vertebr column data use sever machin learn algorithm

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