TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

Design of optimization-based machine learning model with AI for effective breast cancer detection system

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pp. 2213–2225Vol. 45Issue 8November 2024DOI: 10.47974/JIOS-1784XML
Received:
06 Feb 2024
Published Online:
18 Dec 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1784
Pages:
2213–2225

Abstract

Accurate and prompt diagnosis is crucial for early detection and recovery from breast cancer. Many women die from breast cancer because current methods fail to detect it early. To address this challenge, we designed an Artificial Intelligence-based Random Forest (AI-RF) model optimized with Sail Fish Optimization (SFO). Using MRI breast cancer datasets from Kaggle, the system preprocesses data with median filtering and extracts features using the Grey Level Coherence Matrix (GLCM) method. The SFO fitness is updated in the RF model to improve prediction accuracy of breast cancer stages (benign and malignant). The developed model aims to enhance detection accuracy and reduce false rates. Experimental results show the model achieves 99.13% accuracy, 99.33% sensitivity, and 98.85% precision, outperforming existing techniques.

Keywords

Subject Classifications

92B0592C2092C55

References

[1] B. Saylan and S. Cinaroglu, “Opinion mining and machine learning analysis: What emotions Twitter data tell us about telemedicine?” Journal of Statistics and Management Systems, vol. 27, no. 3, pp. 605–633 (2024).
[2] A. Pareek, P. Arora, S. Madan, and N. Gupta, “Telecom customer churn prediction model: Analysis of machine learning techniques for churn prediction and factor identification in the telecom sector,” Journal of Information and Optimization Sciences, vol. 45, no. 2, pp. 613–630 (2024).
[3] K. D. Vidhate, P. Nema, and T. Hasarmani, “Modelling for forecastng energy consumption using SBO optimization and machine learning,” Journal of Information and Optimization Sciences, vol. 45, no. 2, pp. 605–612 (2024).
[4] R. Selvaraj, S. Vidyanandini, and S. R. Nayak, “Radial radio mean labelling of Grotzsch graph and some graphs,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 4, pp. 1377–1387 (2024).
[5] B. K. Padhi, S. Chakravarty, B. Naik, S. R. Nayak, and R. C. Poonia, “Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 4, pp. 1233–1246 (2024).
[6] D. K. J. B. Saini, S. K. Pawar, S. P. Tondare, A. S. Nigade, J. Morbale, and M. Gangwar, “Improve QoS for multi-body sensor analytics in smart healthcare system using machine learning algorithm,” Journal of Interdisciplinary Mathematics, vol. 26, no. 3, pp. 393–405 (2023).
[7] A. Seethalakshmy, T. Tamilvizhi, K. N. Sowjanya, and B. Bhoomeshwar, “Deep learning-based computed tomography image classification of COVID-19 patients,” Journal of Interdisciplinary Mathematics, vol. 26, no. 3, pp. 371–381 (2023).
[8] A. A. Heydari and S. H. Z. Al-Thalabi, “Using a binary logistic regression model to diagnose the effect of factors causing cancer: An applied study on a sample of patients at the Oncology Hospital in Baghdad,” Journal of Statistics and Management Systems, vol. 25, no. 8, pp. 2005–2017 (2022).
[9] A. Yala, P. G. Mikhael, C. Lehman, G. Lin, F. Strand, Y. L. Wan, et al., “Optimizing risk-based breast cancer screening policies with reinforcement learning,” Nature Medicine, vol. 28, no. 1, pp. 136–143 (2022).
[10] E. Michael, H. Ma, H. Li, and S. Qi, “An optimized framework for breast cancer classification using machine learning,” BioMed Research International, vol. 2022, no. 1, p. 8482022 (2022).
[11] S. Punitha, F. Al-Turjman, and T. Stephan, “An automated breast cancer diagnosis using feature selection and parameter optimization in ANN,” Computers & Electrical Engineering (2021).
[12] E. Houssein, M. M. Emam, and A. A. Abdelmgeid, “An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm,” Neural Computing and Applications, pp. 18015–18033 (2022).
[13] Y. Suh, J. J. Jung, and B. Cho, “Automated breast cancer detection in digital mammograms of various densities via deep learning,” Journal of Personalized Medicine, vol. 10, no. 4, p. 211 (2020).

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