Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India.
Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Biomed Res Int. 2022 Jul 7;2022:2318101. doi: 10.1155/2022/2318101. eCollection 2022.
Mesothelioma is a dangerous, violent cancer, which forms a protecting layer around inner tissues such as the lungs, stomach, and heart. We investigate numerous AI methodologies and consider the exact DM conclusion outcomes in this study, which focuses on DM determination. K-nearest neighborhood, linear-discriminant analysis, Naive Bayes, decision-tree, random forest, support vector machine, and logistic regression analyses have been used in clinical decision support systems in the detection of mesothelioma. To test the accuracy of the evaluated categorizers, the researchers used a dataset of 350 instances with 35 highlights and six execution measures. LDA, NB, KNN, SVM, DT, LogR, and RF have precisions of 65%, 70%, 92%, 100%, 100%, 100%, and 100%, correspondingly. In count, the calculated complication of individual approaches has been evaluated. Every process is chosen on the basis of its characterization, exactness, and calculated complications. SVM, DT, LogR, and RF outclass the others and, unexpectedly, earlier research.
间皮瘤是一种危险、暴力的癌症，它在肺部、胃部和心脏等内部组织周围形成一层保护层。我们研究了许多人工智能方法，并考虑了这项专注于 DM 确定的研究中的准确 DM 结论结果。在间皮瘤检测中，K-最近邻、线性判别分析、朴素贝叶斯、决策树、随机森林、支持向量机和逻辑回归分析已被用于临床决策支持系统。为了测试评估分类器的准确性，研究人员使用了一个包含 350 个实例、35 个亮点和六个执行指标的数据集。LDA、NB、KNN、SVM、DT、LogR 和 RF 的精度分别为 65%、70%、92%、100%、100%、100%和 100%。此外，还评估了个别方法的计算复杂性。每个过程都是根据其特征、准确性和计算复杂性来选择的。SVM、DT、LogR 和 RF 优于其他方法，甚至优于早期研究。
