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Updated: Jun 12, 2025

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

Mosa E Hosney1, Essam H Houssein2, Mohammed R Saad1

  • 1Faculty of Computers and Information, Luxor University, Luxor, Egypt.

Computers in Biology and Medicine
|September 25, 2024
PubMed
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This summary is machine-generated.

A new hybrid optimization method, mRIME, enhances bladder cancer (BC) classification accuracy by selecting optimal features. The mRIME-SVM model improves diagnostic outcomes and demonstrates broad applicability in bioinformatics.

Area of Science:

  • Bioinformatics and Computational Biology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Accurate bladder cancer (BC) diagnosis is crucial for effective treatment planning, but challenges remain in classifying tumors from diverse datasets.
  • Existing feature selection (FS) and classification methods may struggle with complex biological data, impacting diagnostic precision.

Purpose of the Study:

  • To introduce a novel hybrid optimization algorithm, mRIME, for wrapper feature selection (FS) in BC diagnosis.
  • To develop an integrated model, mRIME-SVM, combining mRIME for FS and SVM for enhanced BC classification.
  • To evaluate the performance of mRIME and mRIME-SVM on global optimization tasks and BC datasets.

Main Methods:

  • Developed a hybrid optimization algorithm, mRIME, combining Orthogonal Learning (OL) and Rime Optimization Algorithm (RIME) for feature selection.
Keywords:
Cancer classificationFeature selection (FS)Metaheuristics optimizationOrthogonal learning (OL)RIME algorithm

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  • Integrated mRIME with Support Vector Machine (SVM) to create the mRIME-SVM classification model, utilizing mRIME for hyperparameter tuning.
  • Assessed mRIME on the CEC'2022 test suite against various metaheuristic algorithms and evaluated mRIME-SVM on multiple BC datasets.

    • Main Results:

    • The mRIME algorithm demonstrated superior performance in tackling global optimization problems compared to established algorithms.
    • mRIME-SVM achieved high classification accuracy on nine BC datasets, outperforming existing models and popular metaheuristic algorithms.
    • The proposed method effectively navigates complex search spaces, optimizing feature selection without compromising classifier performance.

    Conclusions:

    • The mRIME algorithm offers a competitive and effective approach for diverse optimization tasks, including feature selection.
    • mRIME-SVM provides a robust computational framework for advancing bladder cancer diagnostics with improved accuracy and reliability.
    • This study highlights the potential of hybrid AI approaches in bioinformatics and AI-driven medical research for enhanced disease classification.