Data-driven diagnosis of spinal abnormalities using feature selection and machine learning algorithms

This paper focuses on the application of machine learning algorithms for predicting spinal abnormalities. As a data preprocessing step, univariate feature selection as a filter based feature selection, and principal component analysis (PCA) as a feature extraction algorithm are considered. A number...

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Veröffentlicht in:PloS one Jg. 15; H. 2; S. e0228422
Hauptverfasser: Raihan-Al-Masud, Md, Mondal, M. Rubaiyat Hossain
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 06.02.2020
Public Library of Science (PLoS)
Schlagworte:
Abnormalities
Accuracy
Algorithms
Analysis
Back pain
Bagging
Biology and Life Sciences
Classification
Computer and Information Sciences
Data mining
Datasets
Datasets as Topic - statistics & numerical data
Diagnosis
Diagnosis, Computer-Assisted - methods
Diagnosis, Differential
Feature extraction
Humans
Image Interpretation, Computer-Assisted - methods
Information technology
Learning algorithms
Libraries
Logistic Models
Machine Learning
Medical diagnosis
Medicine and Health Sciences
Methods
Physical Sciences
Posture - physiology
Predictive Value of Tests
Principal components analysis
Recall
Regression analysis
Reproducibility of Results
Research and Analysis Methods
Spinal Diseases - diagnosis
Spinal Diseases - epidemiology
Spine - abnormalities
Spine - diagnostic imaging
Support Vector Machine
Support vector machines
Training
Taiwan
Bangladesh
ISSN:1932-6203, 1932-6203
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Zusammenfassung:This paper focuses on the application of machine learning algorithms for predicting spinal abnormalities. As a data preprocessing step, univariate feature selection as a filter based feature selection, and principal component analysis (PCA) as a feature extraction algorithm are considered. A number of machine learning approaches namely support vector machine (SVM), logistic regression (LR), bagging ensemble methods are considered for the diagnosis of spinal abnormality. The SVM, LR, bagging SVM and bagging LR models are applied on a dataset of 310 samples publicly available in Kaggle repository. The performance of classification of abnormal and normal spinal patients is evaluated in terms of a number of factors including training and testing accuracy, recall, and miss rate. The classifier models are also evaluated by optimizing the kernel parameters, and by using the results of receiver operating characteristic (ROC) and precision-recall curves. Results indicate that when 78% data are used for training, the observed training accuracies for SVM, LR, bagging SVM and bagging LR are 86.30%, 85.47%, 86.72% and 85.06%, respectively. On the other hand, the accuracies for the test dataset for SVM, LR, bagging SVM and bagging LR are the same being 86.96%. However, bagging SVM is the most attractive as it has a higher recall value and a lower miss rate compared to others. Hence, bagging SVM is suitable for the classification of spinal patients when applied on the most five important features of spinal samples.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0228422