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An investigation of machine learning algorithms for prediction of lumbar disc herniation

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Abstract

The prevalence of lumbar disc herniation (LDH), which makes patients’ daily activities more difficult and reduces their quality of life, has tended to increase recently. Many risk factors associated with LDH have been reported. In this study, LDH was predicted using machine learning techniques using measures of the lumbar paraspinal muscles, lumbar vessels cross-sectional area (CSA), and lumbar sagittal curve. Three hundred and forty-four individuals’ MR scans were prospectively enrolled (264 with LDH and 80 healthy). Predictive factors were the lumbar sagittal curve and the cross-sectional areas of the lumbar paraspinal muscles and vessels from sagittal and axial MR images. The measurements have been analyzed via ten different and most common machine learning algorithms by considering a comprehensive parameter tuning and cross-validation process. The variable importance results have been also presented. XGBoost algorithm among all algorithms has provided the best results in terms of different classification metrics including f-score (\(0.830\)), AUC (\(0.939\)), accuracy (\(0.922\)), and kappa (\(0.779\)). The findings of this study demonstrated that cross-sectional areas of the quadratus lumborum and abdominal aorta can be utilized as a reliable indicator of LDH. Consequently, the developed model and the variables found to be important may guide to healthcare professionals to make more accurate and effective decisions in terms of prediction the LDH.

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Acknowledgements

The authors would like to thank to their respective Universities for their support.

Author information

Authors and Affiliations

  1. Physiotherapy and Rehabilitation, Faculty of Health Sciences, Karamanoğlu Mehmetbey University, Karaman, 70100, Turkey

    Hikmet Kocaman

  2. Mathematics, Faculty of Kamil Özdağ Science, Karamanoğlu Mehmetbey University, 70100, Karaman, Turkey

    Hasan Yıldırım

  3. Physiotherapy and Rehabilitation, Faculty of Health Sciences, Tarsus University, Mersin, 33400, Turkey

    Ayşenur Gökşen

  4. Radiology, Karaman Training and Research Hospital, Karamanoğlu Mehmetbey University, Karaman, 70100, Turkey

    Gökçe Merve Arman

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  1. Hikmet Kocaman
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  2. Hasan Yıldırım
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  4. Gökçe Merve Arman
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Contributions

Hikmet Kocaman: conceptualization, investigation, writing-original draft, writing-review editing, validation, supervision. Hasan Yıldırım: methodology, software, formal analysis, visualization, writing-original draft, writing-review editing. Aysenur Goksen: data curation, resources. Gokce Merve Arman: data curation.

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Correspondence to Hikmet Kocaman.

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Ethics approval

The study was approved (No: 2022/11, Date: 03.06.2022) by Tarsus University Ethics Committee and performed following the Declaration of Helsinki. Before the study began, to use their MR images, the informed consent form was approved by each individual. Data collection was applied after the informed consent form was signed.

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Kocaman, H., Yıldırım, H., Gökşen, A. et al. An investigation of machine learning algorithms for prediction of lumbar disc herniation. Med Biol Eng Comput 61, 2785–2795 (2023). https://doi.org/10.1007/s11517-023-02888-x

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