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An efficient improved parrot optimizer for bladder cancer classification.

Essam H Houssein1, Marwa M Emam1, Waleed Alomoush2

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

Computers in Biology and Medicine
|August 30, 2024
PubMed
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This summary is machine-generated.

An improved Parrot Optimizer (IPO) algorithm enhances bladder cancer classification accuracy. The IPO-SVM approach achieved high performance metrics, outperforming other methods for effective bladder cancer detection.

Area of Science:

  • Computational intelligence
  • Medical informatics
  • Machine learning

Background:

  • Bladder cancer (BC) presents significant morbidity and mortality risks.
  • Accurate BC classification is challenging and requires expert analysis.
  • Existing optimization algorithms like the Parrot Optimizer (PO) suffer from limitations such as sub-optimal convergence and high error rates.

Purpose of the Study:

  • To develop an improved optimization algorithm (IPO) to overcome the limitations of the original PO.
  • To enhance the accuracy and efficiency of bladder cancer classification using machine learning.
  • To evaluate the performance of the proposed IPO algorithm against existing methods.

Main Methods:

  • Developed the Improved Parrot Optimizer (IPO) by integrating Mirror Reflection Learning (MRL) and Bernoulli Maps (BMs).
Keywords:
Bladder Cancer (BC)Meta-Heuristics (MH)Mirror Reflection Learning (MRL)Parrot Optimizer (PO)Support Vector Machine (SVM)

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  • Evaluated IPO on CEC 2022 test functions and nine bladder cancer datasets.
  • Integrated IPO with Support Vector Machine (SVM) classifier to create the IPO-SVM approach for BC classification.

    • Main Results:

    • The IPO algorithm ranked first in optimization performance for CEC 2022 functions.
    • The IPO-SVM approach demonstrated superior performance over eight other metaheuristic algorithms on BC datasets.
    • IPO-SVM achieved high classification metrics: 84.11% Accuracy, 98.10% Sensitivity, 95.59% Precision, 95.98% Specificity, and 94.15% F-score.

    Conclusions:

    • The proposed IPO algorithm effectively avoids local optima and improves convergence speed and solution diversity.
    • The IPO-SVM approach offers a promising and effective tool for accurate bladder cancer classification.
    • The developed IPO algorithm has the potential to significantly aid in the early detection and management of bladder cancer.