Comparative Study of Machine Learning Algorithms for Detection of Breast Cancer

Year : 2025 | Volume : 12 | Issue : 02 | Page : 113 129
    By

    Sayli Rajendra Dholam,

  1. Research Scholar, MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Mumbai, Maharashtra, India

Abstract

Breast cancer continues to be the most commonly diagnosed cancer among women, with more than 2.3 million new cases diagnosed yearly worldwide. It is stated as the leading cause of cancer-related deaths. Therefore, this emphasizes the dire necessity for early diagnosis with a view to improving survival. Early diagnosis elevates the effectiveness of prediction and treatment. This research carries out a structured and analytical evaluation of various machine learning algorithms, namely, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, and XGBoost for the purpose of breast cancer detection. This study uses the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, one of the standard criterions sets in this field, to classify tumors into benign or malignant. The performance of these algorithms is evaluated in detail using various metrics, namely, accuracy, precision, recall, F-1 score and confusion matrices, to provide scores useful for proper insight of the said groups in terms of their respective capabilities in carrying out diagnoses.

Keywords: Breast cancer detection, machine learning algorithms, logistic regression, decision tree, random forest, support vector machine (SVM), K-nearest neighbors (KNN), Naïve Bayes, XGBoost, confusion matrix, ROC-AUC analysis, accuracy, precision, recall, F1 score, Wisconsin diagnostic breast cancer (WDBC)

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Sayli Rajendra Dholam. Comparative Study of Machine Learning Algorithms for Detection of Breast Cancer. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):113-129.
How to cite this URL:
Sayli Rajendra Dholam. Comparative Study of Machine Learning Algorithms for Detection of Breast Cancer. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):113-129. Available from: https://journals.stmjournals.com/joaira/article=2025/view=214619


References

  1. Ahmed MT, Imtiaz MN, Karmakar A. Analysis of Wisconsin Breast Cancer original dataset using data mining and machine learning algorithms for breast cancer prediction. J Sci Technol Environ Inform. 2020; 9(2): 665–72.
  2. Akbugday B. Classification of breast cancer data using machine learning algorithms. In 2019 IEEE Medical technologies congress (TIPTEKNO). 2019 Oct 3; 1–4.
  3. Kadhim RR, Kamil MY. Comparison of breast cancer classification models on Wisconsin dataset. Int J Reconfigurable & Embedded Syst. 2022; 11(2): 166–174.
  4. Tahmooresi M, Afshar A, Rad BB, Nowshath KB, Bamiah MA. Early detection of breast cancer using machine learning techniques. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 2018 Sep 26; 10(3–2): 21–7.
  5. Sengar PP, Gaikwad MJ, Nagdive AS. Comparative study of machine learning algorithms for breast cancer prediction. In 2020 IEEE Third International Conference on Smart Systems and Inventive Technology (ICSSIT). 2020 Aug 20; 796–801.
  6. Street WN, Wolberg WH, Mangasarian OL. Nuclear feature extraction for breast tumor diagnosis. In SPIE Biomedical image processing and biomedical visualization. 1993 Jul 29; 1905: 861–870.
  7. Vig L. Comparative analysis of different classifiers for the Wisconsin breast cancer dataset. Open Access Library Journal (OALib Journal). 2014 Sep 1; 1(6): 1–7.
  8. Bazazeh D, Shubair R. Comparative study of machine learning algorithms for breast cancer detection and diagnosis. In 2016 IEEE 5th international conference on electronic devices, systems and applications (ICEDSA). 2016 Dec 6; 1–4.
  9. Shahnaz C, Hossain J, Fattah SA, Ghosh S, Khan AI. Efficient approaches for accuracy improvement of breast cancer classification using wisconsin database. In 2017 IEEE region 10 humanitarian technology conference (R10-HTC). 2017 Dec 21; 792–797.
  10. Obaid OI, Mohammed MA, Ghani MK, Mostafa A, Taha F. Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer. Int J Eng Technol. 2018 Dec; 7(4.36): 160–6.
  11. Austria YD, Jay-ar PL, Maria Jr LB, Goh JE, Goh ML, Vicente HN. Comparison of machine learning algorithms in breast cancer prediction using the coimbra dataset. Int. J. Simulat. Syst. Sci. Tech. 2019; 7(10): 23.1–23.8.
  12. Omondiagbe DA, Veeramani S, Sidhu AS. Machine learning classification techniques for breast cancer diagnosis. In IOP Conf Ser: Mater Sci Eng. IOP Publishing. 2019 Jun 7; 495: 012033.
  13. Mushtaq Z, Yaqub A, Sani S, Khalid A. Effective K-nearest neighbor classifications for Wisconsin breast cancer data sets. J Chin Inst Eng. 2020 Jan 2; 43(1): 80–92.
  14. Naji MA, El Filali S, Aarika K, Benlahmar EH, Ait Abdelouhahid R, Debauche O. Machine learning algorithms for breast cancer prediction and diagnosis. Procedia Comput Sci. 2021 Jan 1; 191: 487–92.
  15. Sakib S, Yasmin N, Tanzeem AK, Shorna F, Md. Hasib K, Alam SB. Breast cancer detection and classification: A comparative analysis using machine learning algorithms. In Proceedings of Third International Conference on Communication, Computing and Electronics Systems: ICCCES 2021. Singapore: Springer Singapore; 2022 Mar 20; 703–717.
  16. Khan RH, Miah J, Rahman MM, Tayaba M. A comparative study of machine learning algorithms for detecting breast cancer. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC). 2023 Mar 8; 647–652.
  17. Kushwaha V. Breast cancer diagnostic using machine learning: applying supervised learning techniques to Coimbra and Wisconsin datasets. Finland: Lappeenranta–Lahti University of Technology LUT; 2023.
  18. Hossin MM, Shamrat FJ, Bhuiyan MR, Hira RA, Khan T, Molla S. Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset. Bull Electr Eng Info. 2023 Aug 1; 12(4): 2446–56.
Regular Issue Subscription Original Research
Volume 12
Issue 02
Received 06/03/2025
Accepted 12/04/2025
Published 25/06/2025
Publication Time 111 Days
297

Login


My IP

PlumX Metrics