Published January 30, 2025 | Version CC-BY-NC-ND 4.0
Journal Open

Early Detection of Breast Cancer: Comparative Analysis of Machine Learning and Deep Learning Algorithms

Authors/Creators

  • 1. Department of Computer Science and Engineering, Chandigarh University, Chandigarh (Punjab), India.

Contributors

Contact person:

  • 1. Department of Computer Science and Engineering, Chandigarh University, Chandigarh (Punjab), India.

Description

Abstract: Breast cancer classification remains a critical challenge in medical diagnostics due to the imbalanced nature of available datasets, where the minority (cancerous malignant) class is often overshadowed by the majority (benign) class. This study proposes a hybrid model based on logistic regression, enhanced with class balancing techniques and ant search optimization, to improve the identification of the malignant class. The model is compared with SVM, Random Forest, and KNearest Neighbors (KNN) across three stages: prediction before diagnosis, at diagnosis and therapy, and post-treatment outcomes. The experiments, conducted on the Jupyter platform using the Wisconsin breast cancer dataset, demonstrate that the hybrid model achieves a high accuracy of 92.98%, significantly reducing false negatives. The study highlights the strengths of logistic regression in providing interpretable results, crucial for clinical decision-making, especially when compared to more complex models like Artificial Neural Networks (ANN). This research offers a reliable and accurate tool for early breast cancer detection and prognosis, contributing to ongoing efforts to enhance patient outcomes through the integration of hybrid machine learning models in medical diagnostics.

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Additional details

Identifiers

EISSN
2582-7588
DOI
10.54105/ijpmh.B1047.05020125

Dates

Accepted
2025-01-15
Manuscript received on 07 November 2024 | First Revised Manuscript received on 14 November 2024 | Second Revised Manuscript received on 01 December 2024 | Manuscript Accepted on 15 January 2025 | Manuscript published on 30 January 2025

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