Efficient bladder cancer diagnosis using an improved RIME algorithm with Orthogonal Learning

• Published in: Computers in biology and medicine Vol. 182; p. 109175
• Main Authors: Hosney, Mosa E., Houssein, Essam H., Saad, Mohammed R., Samee, Nagwan Abdel, Jamjoom, Mona M., Emam, Marwa M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.11.2024; Elsevier Limited
• Subjects: Accuracy; Algorithms; Bioinformatics; Bladder; Bladder cancer; Cancer; Cancer classification; Classification; Competitiveness; Datasets; Diagnosis; Diagnosis, Computer-Assisted - methods; Effectiveness; Evolutionary computation; Feature selection (FS); Global optimization; Heuristic methods; Humans; Internal Medicine; Machine Learning; Medical diagnosis; Medical research; Metaheuristics optimization; Optimization algorithms; Orthogonal learning (OL); Other; Performance evaluation; RIME algorithm; Search process; Support Vector Machine; Support vector machines; Urinary Bladder Neoplasms - diagnosis; Orthogonal learning (OL); Feature selection (FS); Cancer classification; RIME algorithm; Metaheuristics optimization
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.109175

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. •A new wrapper FS method combines RIME wit OL is proposed.•CEC’2022 suite is used for verification of results.•The mRIME algorithm is combined with SVM classifier (mRIME-SVM) for Bladder cancer classification.•Experimental results confirm the performance and effectiveness of the mRIME-SVM in practice.•The proposed method is compared with state-of-the-art algorithms.