Exploring the Machine Learning Techniques in Early Detection of Breast Cancer

Exploring the Machine Learning Techniques in Early Detection of Breast Cancer

Authors

  • Aaditya Singh
  • Shrutika Ohol
  • Sakshi Suryarao
  • Prof. Dr. Gufran Ahmad Ansari

Keywords:

Breast Cancer, Machine Learning, Data Analytics

Abstract

Women frequently get breast cancer, and early detection is key to improving patient outcomes. Recently, machine learning techniques have showed promise in improving the accuracy and efficacy of breast cancer diagnosis. In this study, we analyze various machine learning techniques, such as logistic regression, decision trees, random forests, support vector machines, artificial neural networks, and deep learning, and its use in the early identification of breast cancer. We look at the challenges of applying these techniques and highlight the importance of large datasets for creating and testing machine learning models. We also discuss conventional methods for detecting breast cancer and its limitations, highlighting the promise of machine learning technologies to move past these limitations. Our results suggest that machine learning techniques might improve the accuracy of breast cancer detection and aid in early diagnosis, leading to better patient outcomes.

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References

“Breast Cancer Wisconsin (Diagnosti c) Dataset (BCWD)” https://www.kaggle.com/-datasets/uciml/breast-cancer-wisconsin-data

Ali Bou Nassif*, M. A. (2022). Breast cancer detection using artificial intelligence techniques: A systematic literature review. Elsevier.

Alsaleem MA, B. G. (2020). A novel prognostic two-gene signature for triple negative breast cancer. Retrieved from doi.org: https://doi.org/10.1038/s41379-020-0563-7.

Gufran Ahmad Ansari, S. S. (2021). Early Prediction of Diabetes Disease & Classification of Algorithms Using. SSRN Electronic Journal.

Gufran Ahmad Ansari, S. S. (2021). Predictions of Diabetes and Diet Recommendation System for Diabetic Patients. 2021 2nd International Conference for Emerging Technology (INCET).

Simidjievski N, B. C. (2019). research paper- variational autoencoders for Cancer Data Integration: design Principles and Computational Practice. Retrieved from https://doi.org/10.3389/fgene.2019.01205.

Sun D, W. M. (2019). research paper- A Multimodal Deep Neural Network for Human Breast Cancer prognosis Prediction by Integrating Multi-Dimensional Data. Retrieved from doi.org: https://doi.org/10.1109/TCBB.2018.2806438

Additional Files

Published

30-05-2023

How to Cite

Aaditya Singh, Shrutika Ohol, Sakshi Suryarao, & Prof. Dr. Gufran Ahmad Ansari. (2023). Exploring the Machine Learning Techniques in Early Detection of Breast Cancer. Vidhyayana - An International Multidisciplinary Peer-Reviewed E-Journal - ISSN 2454-8596, 8(si7), 44–55. Retrieved from https://vidhyayanaejournal.org/journal/article/view/805
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