The week's pick
Reseach Article
The Performance of K-Nearest Neighbors on Malignant and Benign Classes: Sensitivity, Specificity, and Accuracy Analysis for Breast Cancer Diagnosis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 180 - Number 8 |
| Year of Publication: 2017 |
| Authors: Arash Roshanpoor, Marjan Ghazisaeidi, Sharareh R. Niakan Kalhor, Keyvan Maghooli, Reza Safdari |
10.5120/ijca2017916069
|
Arash Roshanpoor, Marjan Ghazisaeidi, Sharareh R. Niakan Kalhor, Keyvan Maghooli, Reza Safdari . The Performance of K-Nearest Neighbors on Malignant and Benign Classes: Sensitivity, Specificity, and Accuracy Analysis for Breast Cancer Diagnosis. International Journal of Computer Applications. 180, 8 ( Dec 2017), 33-37. DOI=10.5120/ijca2017916069
Abstract
Breast cancer is one of the major threats to women nowadays. Early detection of breast cancer decreases mortality rate. Machine learning algorithms are used for this purpose. Accuracy is the most popular measure for evaluating machine learning algorithms for breast cancer diagnosis. However, it does not make a distinction between the performance of the classifier on malignant and benign test cases. This paper studies sensitivity and specificity along with accuracy to differentiate between KNN performance on malignant and benign classes for the different number of neighbors. Additionally, the standard deviations of sensitivity and specificity are studied to show KNN stability in malignant and benign classes. This study is critical because the cost of false negative is more than the cost of false positive in breast cancer detection. This study is conducted on Wisconsin breast cancer dataset (WBCD) from UCI repository. Stratified 10-fold cross-validation is used in this paper. Additionally, in order to increase the correctness of outcome, validation method repeated 100 times by considering that the samples are randomly reassigned to the folds again. The results show that KNN does not work well on malignant samples compared to the benign test cases, especially for higher values of neighbors. Also, the results for malignant samples are less reliable than benign ones. Furthermore, accuracy is more representative of specificity than sensitivity. It seems that the imbalance distributions of malignant and benign classes make difference between KNN performance on malignant and benign samples. It is recommended that a new study to be conducted to show the effect of imbalance numbers of positive and negative samples and also the difference between standard deviations of positive and negative classes on KNN performance.
References
- Senturk ZK, Kara R. Breast Cancer Diagnosis via Data Mining: Performance Analysis of Seven different algorithms. Comput Sci Eng. 2014;4(1):35.
- Odajima K, Pawlovsky AP. A detailed description of the use of the kNN method for breast cancer diagnosis. In: Biomedical Engineering and Informatics (BMEI), 2014 7th International Conference. IEEE; 2014 May 27. p. 688–692.
- Medjahed SA, Saadi TA, Benyettou A. Breast Cancer Diagnosis by using k-Nearest Neighbor with Different Distances and Classification Rules. Int J Comput Appl. 2013;62(1).
- Gayathri BM, Sumathi CP, Santhanam T. Breast Cancer Diagnosis using Machine Learning Algorithms-A Survey. Int J Distrib Parallel Syst. 2013;4(3):105.
- Salama GI, Abdelhalim M, Zeid MA. Breast cancer diagnosis on three different datasets using multi-classifiers. Breast Cancer WDBC. 2012;32(569):2.
- You H, Rumbe G. Comparative study of classification techniques on breast cancer FNA biopsy data. IJIMAI. 2010;1(3):6–13.
- Gupta S, Kumar D, Sharma A. Data Mining Classification Techniques Applied For Breast Cancer Diagnosis And Prognosis.
- Sarkar M, Leong T-Y. Application of K-nearest neighbors algorithm on breast cancer diagnosis problem. In: Proceedings of the AMIA Symposium. American Medical Informatics Association; 2000. p. 759.
- Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2006;2.
- Pena-Reyes CA, Sipper M. A fuzzy-genetic approach to breast cancer diagnosis. Artif Intell Med. 1999;17(2):131–155.
- Karabatak M, Ince MC. An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl. 2009;36(2):3465–3469.
- Jhajharia S, Verma S, Kumar R. Predictive analytics for breast cancer survivability: A comparison of five predictive models. In: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies. ACM; 2016. p. 26.
- Rahman MM, Davis DN. Addressing the Class Imbalance Problem in Medical Datasets. Int J Mach Learn Comput. 2013;224–8.
- Das B, Krishnan NC, Cook DJ. Handling class overlap and imbalance to detect prompt situations in smart homes. In: 2013 IEEE 13th International Conference on Data Mining Workshops. IEEE; 2013. p. 266–273.
- Gou J, Du L, Zhang Y, Xiong T. A New Distance-weighted k-nearest Neighbor Classifier. J Inf Comput Sci. 9(6):1429–1436.
- Jain R, Mazumdar J. A genetic algorithm based nearest neighbor classification to breast cancer diagnosis. Australas Phys Eng Sci Med. 2003;26(1):6–11.
- Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, et al. Top 10 algorithms in data mining. Knowl Inf Syst. 2008;14(1):1–37.
- UCI Machine Learning Repository: Data Sets [Internet]. [cited 2016 Sep 13]. Available from: https://archive.ics.uci.edu/ml/datasets.html
- Han and Kamber: Data Mining---Concepts and Techniques, 2nd ed., Morgan Kaufmann, 2006.
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