Faculty of Computers and Information, Luxor University, Luxor, Egypt.
Faculty of Computers and Information, Minia University, Minia, Egypt.
Comput Biol Med. 2024 Nov;182:109175. doi: 10.1016/j.compbiomed.2024.109175. Epub 2024 Sep 24.
Bladder cancer (BC) diagnosis presents a critical challenge in biomedical research, necessitating accurate tumor classification from diverse datasets for effective treatment planning. This paper introduces a novel wrapper feature selection (FS) method that leverages a hybrid optimization algorithm combining Orthogonal Learning (OL) with a rime optimization algorithm (RIME), termed mRIME. The mRIME algorithm is designed to avoid local optima, streamline the search process, and select the most relevant features without compromising classifier performance. It also introduces mRIME-SVM, a novel hybrid model integrating modified mRIME for FS with Support Vector Machine (SVM) for classification. The mRIME algorithm is employed as an FS method and is also utilized to fine-tune the hyperparameters of it the It SVM, enhancing the overall classification accuracy. Specifically, mRIME navigates complex search spaces to optimize FS without compromising classifier performance. Evaluated on eight diverse BC datasets, mRIME-SVM outperforms popular metaheuristic algorithms, ensuring precise and reliable diagnostic outcomes. Moreover, the proposed mRIME was employed for tackling global optimization problems. It has been thoroughly assessed using the IEEE Congress on Evolutionary Computation 2022 (CEC'2022) test suite. Comparative analyzes with Gray wolf optimization (GWO), Whale optimization algorithm (WOA), Harris hawks optimization (HHO), Golden Jackal Optimization (GJO), Hunger Game optimization algorithm (HGS), Sinh Cosh Optimizer (SCHO), and the original RIME highlight mRIME's competitiveness and efficacy across diverse optimization tasks. Leveraging mRIME's success, mRIME-SVM achieves high classification accuracy on nine BC datasets, surpassing existing models. Results underscore mRIME's competitiveness and applicability across diverse optimization tasks, extending its utility to enhance BC classification. This study contributes to advancing BC diagnostics with a robust computational framework, promising broader applications in bioinformatics and AI-driven medical research.
膀胱癌 (BC) 的诊断在生物医学研究中是一个极具挑战性的问题，需要从各种数据集中准确地对肿瘤进行分类，以制定有效的治疗计划。本文介绍了一种新的包装特征选择 (FS) 方法，该方法利用了一种结合正交学习 (OL) 和鲸鱼优化算法 (RIME) 的混合优化算法，称为 mRIME。mRIME 算法旨在避免局部最优，简化搜索过程，选择最相关的特征，而不会影响分类器的性能。它还引入了 mRIME-SVM，这是一种将改进的 mRIME 用于 FS 与支持向量机 (SVM) 分类相结合的新型混合模型。mRIME 算法被用作 FS 方法，也用于微调 SVM 的超参数，从而提高整体分类准确性。具体来说，mRIME 在不影响分类器性能的情况下，在复杂的搜索空间中进行导航以优化 FS。在八个不同的 BC 数据集上进行评估，mRIME-SVM 优于流行的元启发式算法，确保了精确和可靠的诊断结果。此外，所提出的 mRIME 用于解决全局优化问题。它已经使用 2022 年电气和电子工程师协会进化计算大会 (CEC'2022) 测试套件进行了彻底评估。与灰狼优化 (GWO)、鲸鱼优化算法 (WOA)、蜂鹰优化算法 (HHO)、金豺优化算法 (GJO)、饥饿游戏优化算法 (HGS)、双曲正弦优化器 (SCHO) 和原始 RIME 的比较分析突出了 mRIME 在各种优化任务中的竞争力和功效。利用 mRIME 的成功，mRIME-SVM 在九个 BC 数据集上实现了高分类准确性，超过了现有模型。结果强调了 mRIME 在各种优化任务中的竞争力和适用性，将其应用扩展到增强 BC 分类。本研究为膀胱癌诊断提供了一个强大的计算框架，有望在生物信息学和人工智能驱动的医学研究中得到更广泛的应用。
