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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 6

Published on: June 2026

PREDICTION OF BREAST CANCER, COMPARATIVE REVIEW OF MACHINE LEARNING TECHNIQUES, AND THEIR ANALYSIS

G. ANUSHA G. NITHISH P. SUMANJALI

SK. Sharif

Department of CSE (AI & ML)

CMR Technical Campus, Hyderabad
DOI:https://doi.org/10.55041/ijcope.v2i6.208

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Abstract

Breast cancer remains one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate prediction plays a crucial role in improving patient outcomes and survival rates. With the advancement of computational intelligence, machine learning (ML) techniques have emerged as powerful tools for disease prediction and diagnosis. This study provides a comprehensive comparative review of various machine learning algorithms applied to breast cancer prediction, including Support Vector Machines (SVM), Decision Trees, Random Forest, K-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression, and Neural Networks. The performance of these models is evaluated based on key metrics such as accuracy, precision, recall, F1-score, and AUC-ROC values using standard datasets such as the Wisconsin Breast Cancer Dataset. Our analysis highlights the strengths and limitations of each method, with ensemble models and deep learning approaches showing higher prediction accuracy and robustness. The review also discusses preprocessing techniques, feature selection, and the importance of balanced datasets in improving model performance.

How to Cite this Paper

ANUSHA, G., NITHISH, G. & SUMANJALI, P. (2026). Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and their Analysis. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.208

ANUSHA, G., et al.. "Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and their Analysis." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.208.

ANUSHA, G.,G. NITHISH, and P. SUMANJALI. "Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and their Analysis." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.208.

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  • •Review follows editorial policy.
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  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Jun 16 2026
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