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
Online-Zugang:	Volltext
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