Department of Electronics & Communication Engineering, ATME College of Engineering, Mysuru, India.
Ir J Med Sci. 2024 Apr;193(2):571-575. doi: 10.1007/s11845-023-03487-3. Epub 2023 Aug 19.
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.
脊髓肿瘤被认为是一种异质性疾病，由许多不同的亚型组成。癌症早期诊断和预后已成为癌症研究的必要条件，因为这有助于患者后续的临床管理。将癌症患者分为良性或恶性的重要性促使许多来自生物医学和生物信息学领域的研究团队研究机器学习 (ML) 方法的应用。因此，这些技术已被用于对癌症状况的进展和治疗进行建模。此外，ML 工具从复杂数据集检测关键特征的能力凸显了其重要性。Logistic 回归、支持向量机 (SVM)、决策树 (DT)、随机森林分类器 (RF) 等多种技术已广泛应用于癌症研究中，以开发预测模型，从而实现有效的准确决策。尽管使用 ML 方法可以提高我们对癌症进展的理解，但为了使这些方法在日常临床实践中得到考虑，需要进行适当水平的验证。在这项工作中，我们讨论了一种基于各种监督 ML 技术的预测模型。
