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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| Vydáno v: | PloS one Ročník 15; číslo 2; s. e0228422 |
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06.02.2020
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| Abstract | 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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| AbstractList | 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.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. 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. |
| Audience | Academic |
| Author | Mondal, M. Rubaiyat Hossain Raihan-Al-Masud, Md |
| AuthorAffiliation | Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh Universidad de Zaragoza, SPAIN |
| AuthorAffiliation_xml | – name: Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh – name: Universidad de Zaragoza, SPAIN |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32027680$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/S0140-6736(11)60937-9 10.1093/ageing/afj055 10.1016/j.apmr.2013.10.032 10.1162/089976603321891855 10.1371/journal.pone.0118432 10.1016/j.eswa.2005.09.045 10.1007/978-3-642-21257-4_73 10.1097/00024720-200210000-00007 10.1093/bioinformatics/btm344 10.1109/ICHI.2016.90 10.1109/ICASSDA.2018.8477622 10.1155/2011/876306 10.1109/WICT.2014.7077287 10.1080/00140138508963114 10.1111/ner.12018 10.1001/jama.280.2.147 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2020 Public Library of Science 2020 Raihan-Al-Masud, Mondal. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 Raihan-Al-Masud, Mondal 2020 Raihan-Al-Masud, Mondal |
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| SubjectTerms | 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 |
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| Title | Data-driven diagnosis of spinal abnormalities using feature selection and machine learning algorithms |
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