Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer
-
Irem Ozcan
Department of Computer Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Istanbul, Turkey -
Hakan Aydın
Department of Computer Engineering, Faculty of Engineering, Istanbul Topkapı University, Istanbul, Turkey -
Ali Çetinkaya
Department of Electronics Technology, Istanbul Gelisim Vocational School, Istanbul Gelisim University, Istanbul, Turkey
For Correspondence: alcetinkaya@gelisim.edu.tr
- Received: November 23, 2021
- Accepted: December 25, 2022
- DOI: 10.31557/APJCP.2022.23.10.3287
- Full Article Access Link: Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer
Abstract
Objective: To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. Methods: The “College of Wisconsin Breast Cancer Dataset”, which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the dataset was preprocessed. Sensitivity, accuracy, and definiteness metrics were used to measure the success of the methods. Result: Compared to other ML algorithms used in the study, the GBC ML algorithm was found to be the most successful method in the classification of tumors with an accuracy of 99.12%. The XGB ML algorithm was found to be the lowest method with an accuracy rate of 88.10%. In addition, it was determined that the general accuracy rates of the 11 ML algorithms used in the study varied between 88-95%.Conclusion: When the results obtained from the ML classifiers used in the study are evaluated, the efficiency of the GBC algorithm in the classification of tumors is obvious. It can be said that the success rates obtained from 11 different ML algorithms used in the study are valuable in terms of being used to predict different cancer types.
Keywords: Machine Learning, Breast Cancer, Classification, Data Management, Information Systems
-
IEEE: I. Ozcan, H. Aydin, and A. Cetinkaya, “Comparison of classification success rates of different machine learning algorithms in the diagnosis of breast cancer,” Asian Pacific Journal of Cancer Prevention, vol. 23, no. 10, pp. 3287–3297, Oct. 2022, doi: 10.31557/apjcp.2022.23.10.3287.
-
APA: Ozcan, I., Aydin, H., & Cetinkaya, A. (2022). Comparison of classification success rates of different machine learning algorithms in the diagnosis of breast cancer. Asian Pacific Journal of Cancer Prevention: APJCP, 23(10), 3287.
-
MLA: Ozcan, Irem, Hakan Aydin, and Ali Cetinkaya. "Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer." Asian Pacific Journal of Cancer Prevention, vol. 23, no. 10, 2022, pp. 3287-3297. doi: 10.31557/APJCP.2022.23.10.3287.
This work is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License, allowing non-commercial sharing and adaptation with proper attribution.
