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Related Experiment Video

Updated: Nov 10, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and

Shafaq Abbas¹, Zunera Jalil², Abdul Rehman Javed²

¹Department of Computer Science, Air University, Islamabad, Pakistan.

Peerj. Computer Science

|April 5, 2021

Summary

This summary is machine-generated.

A new machine learning method, Breast Cancer Detection using Extremely Randomized Tree and Whale Optimization Algorithm (BCD-WERT), significantly improves breast cancer prediction accuracy. This approach enhances early detection and patient outcomes by efficiently selecting relevant features.

Keywords:

Breast cancer Machine learning Support vector machine Whale optimization algorithm

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Last Updated: Nov 10, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

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Published on: October 11, 2018

Area of Science:

Oncology
Computer Science
Bioinformatics

Background:

Breast cancer is a leading cause of mortality globally, affecting 1 in 8 women.
Current treatments are often invasive and costly, impacting patient quality of life.
Accurate early prediction is crucial for improving survival rates and treatment efficacy.

Purpose of the Study:

To introduce a novel machine learning approach for enhanced breast cancer prediction.
To leverage feature selection techniques for improved classification accuracy.
To evaluate the proposed method against existing state-of-the-art algorithms.

Main Methods:

Developed the Breast Cancer Detection using Extremely Randomized Tree and Whale Optimization Algorithm (BCD-WERT).
Utilized Whale Optimization Algorithm (WOA) for dimensionality reduction and feature selection.
Implemented and compared BCD-WERT with eight other machine learning algorithms.

Main Results:

BCD-WERT achieved the highest accuracy rate of 99.30% in breast cancer prediction.
The proposed method significantly outperformed Support Vector Machine (SVM) and other algorithms.
Feature selection techniques were demonstrated to be effective in enhancing prediction accuracy.

Conclusions:

BCD-WERT offers a highly accurate and efficient solution for breast cancer prediction.
The study highlights the importance of advanced feature selection in machine learning for medical diagnostics.
This novel approach holds potential for improving early detection and patient management in breast cancer care.