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Comparison of machine learning algorithms for the classification of spinal cord tumor

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Abstract

Spinal cord Tumor has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into Bening or malignant has led many re- search teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, Logistic regression, Support Vector Machines (SVMs), Decision Trees (DTs), Random forest classifier(RFs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we have discussed a predictive model based on various supervised ML techniques.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to acknowledge the principal and the management of ATME College of Engineering, Mysore, for their continued support and help.

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Authors and Affiliations

  1. Department of Electronics & Communication Engineering, ATME College of Engineering, Mysuru, India

    Sheetal Garg & Bhagyashree Raghavan

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  1. Sheetal Garg
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Contributions

Sheetal Garg conceived the original idea. She analyzed and interpreted the patient MRIs. She designed the model and the computational framework and analyzed the data. All authors read and approved the final manuscript. Bhagyashree S R supervised the project.

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Correspondence to Sheetal Garg.

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Garg, S., Raghavan, B. Comparison of machine learning algorithms for the classification of spinal cord tumor. Ir J Med Sci 193, 571–575 (2024). https://doi.org/10.1007/s11845-023-03487-3

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