脊髓肿瘤被认定为一种由许多不同亚型组成的异质性疾病。癌症类型的早期诊断和预后已成为癌症研究中的必要条件，因为它能够促进随后的临床患者管理。将癌症患者分类为良性或恶性的重要性已经引领了生物医学和生物信息学领域的许多研究团队去研究机器学习（ML）方法的应用。因此，这些技术已被用于模拟癌症病情的进展和治疗。此外，ML工具能够从复杂数据集中检测关键特征，显示了它们的重要性。各种技术，如逻辑回归、支持向量机（SVM）、决策树（DT）和随机森林（RF）分类器，已经广泛应用于癌症研究中，用于开发预测模型，从而实现有效和准确的决策。尽管明显使用ML方法可以提高我们对癌症进展的理解，但这些方法被应用于日常临床实践中需要适当的验证。在本研究中，我们讨论了一种基于多种监督性ML技术的预测模型。© 2023作者（们），在独家许可下授予爱尔兰皇家医学学院使用。

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.© 2023. The Author(s), under exclusive licence to Royal Academy of Medicine in Ireland.