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Stream/Area:
-
Subject:
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Paper ID:
IJSREM49408
Volume:
9 |
6 |
Jun 2025
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Dept. of Computer Science & Engineering
RTC Institute of Technology, Ranchi, India
Assistant Professor, Dept. of Computer Science & Engineering
RTC Institute of Technology Ranchi, India
Breast cancer is among the most prevalent cancers affecting women worldwide and remains a major cause of mortality. Early diagnosis significantly improves treatment success and survival rates. Machine learning techniques have emerged as powerful tools in medical diagnosis for detecting complex disease patterns from healthcare datasets. This study presents a comparative analysis of three supervised machine learning algorithms, namely Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN), for breast cancer detection using numerical features extracted from breast cancer biopsy data. The Wisconsin Breast Cancer Dataset is utilized for experimentation. Data preprocessing, feature normalization, exploratory data analysis, model training, and performance evaluation were performed. The proposed models achieved classification accuracies of 94.74%, 96.49%, and 98.24% respectively. The ANN model exhibited superior performance, demonstrating its capability to capture highly nonlinear relationships within medical data. The research highlights the potential of machine learning systems in supporting healthcare professionals for early breast cancer diagnosis and reducing misclassification rates.
KEYWORDS
This research investigated the effectiveness of Decision Tree, Support Vector Machine, and Artificial Neural Network models for breast cancer classification. Experimental analysis demonstrated that ANN achieved the highest accuracy of 98.24%, followed by SVM and Decision Tree. The study confirms that machine learning techniques can play an essential role in supporting healthcare professionals through accurate, reliable, and early cancer diagnosis. The proposed approach has the potential to enhance clinical decision-making and improve patient outcomes.
[1] A. R. Chaudhury et al., “Diagnosis of Invasive Ductal Carcinoma Using Image Processing Techniques,” Proc. International Conference on Medical Imaging, 2011. [2] F. K. Ahmad and N. Yusoff, “Breast Cancer Classification Using Random Forest,” Journal of Clinical Analytics, 2013. [3] A. Dey, “Machine Learning Algorithms: A Review,” Computer Science Review, vol. 4, pp. 23–31, 2016. [4] H. Sharma and S. Kumar, “Survey on Decision Tree Algorithms,” International Journal of Computer Applications, 2016. [5] A. Esteva et al., “Dermatologist-level Classification of Skin Cancer,” Nature, vol. 542, pp. 115–118, 2017. [6] Y. Khourdifi and M. Bahaj, “Machine Learning Algorithms for Breast Cancer Prediction,” IEEE Insurance & Healthcare Systems, 2018.
Publication Date
6/25/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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