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Updated: Dec 29, 2025

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

Md Raihan-Al-Masud1, M Rubaiyat Hossain Mondal1

  • 1Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

Plos One
|February 7, 2020
PubMed
Summary
This summary is machine-generated.

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Machine learning accurately predicts spinal abnormalities using support vector machines (SVM) and logistic regression (LR). Bagging SVM demonstrated superior performance, offering higher recall and lower miss rates for spinal abnormality classification.

Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Spinal Diagnostics

Background:

  • Accurate prediction of spinal abnormalities is crucial for timely diagnosis and treatment.
  • Machine learning offers promising tools for analyzing complex medical data and improving diagnostic accuracy.
  • Existing methods require robust feature selection and classification techniques for effective spinal abnormality detection.

Purpose of the Study:

  • To evaluate the efficacy of various machine learning algorithms for predicting spinal abnormalities.
  • To compare the performance of Support Vector Machine (SVM), Logistic Regression (LR), and their bagging ensemble variants.
  • To identify the optimal model and feature set for classifying normal versus abnormal spinal patients.

Main Methods:

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  • Applied univariate feature selection and Principal Component Analysis (PCA) for data preprocessing.
  • Implemented and compared SVM, LR, bagging SVM, and bagging LR models on a 310-sample dataset.
  • Evaluated model performance using accuracy, recall, miss rate, ROC curves, and precision-recall curves.

    • Main Results:

    • All tested models achieved comparable test accuracies around 86.96% with 78% training data.
    • Bagging SVM exhibited a higher recall and a lower miss rate compared to individual SVM, LR, and bagging LR.
    • Optimized kernel parameters and feature selection identified the top five features for classification.

    Conclusions:

    • Bagging SVM is the most suitable algorithm for classifying spinal abnormalities, particularly when using the top five predictive features.
    • Machine learning, especially ensemble methods like bagging SVM, significantly enhances the accuracy of spinal abnormality diagnosis.
    • The study validates the potential of PCA and feature selection in conjunction with ML for medical data analysis.