An Investigation on the Use of Clustering Algorithms for Data Preprocessing in Breast Cancer Diagnosis

Year 2024, , 70 - 77, 26.03.2024

Ali Şenol , Mahmut Kaya

https://doi.org/10.46810/tdfd.1364397

Abstract

Classification algorithms are commonly used as a decision support system for diagnosing many diseases, such as breast cancer. The accuracy of classification algorithms can be affected negatively if the data contains outliers and/or noisy data. For this reason, outlier detection methods are frequently used in this field. In this study, we propose and compare various models that use clustering algorithms to detect outliers in the data preprocessing stage of classification to investigate their effects on classification accuracy. Clustering algorithms such as DBSCAN, HDBSCAN, OPTICS, FuzzyCMeans, and MCMSTClustering (MCMST) were used separately in the data preprocessing stage of the k Nearest Neighbor (kNN) classification algorithm for outlier elimination, and then the results were compared. According to the obtained results, MCMST algorithm was more successful in outlier elimination. The classification accuracy of the kNN + MCMST model was 0.9834, which was the best one, while the accuracy of kNN algorithm without using any data preprocessing was 0.9719.

Keywords

Outlier detection, clustering, classification, breast cancer diagnosis

References

• Sağlık, A. Rakamlarla Meme Kanseri. 2023 [cited 2023 12.09.2023]; Available from: https://www.anadolusaglik.org/blog/rakamlarla-meme-kanseri.
• Şenol, A., Canbay, Y. and Kaya, M., Trends in Outbreak Detection in Early Stage by Using Machine Learning Approaches. Bilişim Teknolojileri Dergisi. 14(4): p. 355-366.
• Khaire, U.M. and R. Dhanalakshmi, Stability of feature selection algorithm: A review. Journal of King Saud University-Computer Information Sciences, 2022. 34(4): p. 1060-1073.
• Zhou, H., X. Wang, and R. Zhu, Feature selection based on mutual information with correlation coefficient. Applied Intelligence, 2022: p. 1-18.
• Heidari, A., et al., Machine learning applications for COVID-19 outbreak management. Neural Computing Applications, 2022. 34(18): p. 15313-15348.
• Deiana, A.M., et al., Applications and techniques for fast machine learning in science. 2022. 5: p. 787421.
• Russell, S.J., Artificial intelligence a modern approach. 2010: Pearson Education, Inc.
• Manevitz, L.M. and M. Yousef, One-class SVMs for document classification. Journal of machine Learning research, 2001. 2(Dec): p. 139-154.
• Ali, N., D. Neagu, and P. Trundle, Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets. SN Applied Sciences, 2019. 1: p. 1-15.
• Fürnkranz, J., Decision Tree, in Encyclopedia of Machine Learning and Data Mining, C. Sammut and G.I. Webb, Editors. 2017, Springer US: Boston, MA. p. 330-335.
• Jain, A.K., J. Mao, and K.M. Mohiuddin, Artificial neural networks: A tutorial. J Computer, 1996. 29(3): p. 31-44.
• Liu, F.T., K.M. Ting, and Z.-H. Zhou, Isolation-Based Anomaly Detection. ACM Trans. Knowl. Discov. Data, 2012. 6(1): p. Article 3.
• Breunig, M.M., et al., LOF: identifying density-based local outliers. SIGMOD Rec., 2000. 29(2): p. 93–104.
• Schölkopf, B., et al., Estimating the support of a high-dimensional distribution. Neural Computation, 2001. 13(7): p. 1443-1471.
• Rousseeuw, P.J. and C. Croux, Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 1993. 88(424): p. 1273-1283.
• Ahmad, S., et al., On efficient monitoring of process dispersion using interquartile range. Open journal of applied sciences, 2012. 2(04): p. 39-43.
• Hartigan, J.A. and M.A. Wong, A k-means clustering algorithm. JSTOR: Applied Statistics, 1979. 28(1): p. 100--108.
• Ester, M., et al., A density-based algorithm for discovering clusters in large spatial databases with noise, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. 1996, AAAI Press: Portland, Oregon. p. 226-231.
• Campello, R.J.G.B., D. Moulavi, and J. Sander. Density-Based Clustering Based on Hierarchical Density Estimates. in Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2013.
• Ankerst, M., et al., OPTICS: ordering points to identify the clustering structure. SIGMOD Rec., 1999. 28(2): p. 49–60.
• Bezdek, J.C., R. Ehrlich, and W. Full, FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 1984. 10(2): p. 191-203.
• Şenol, A., MCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters. Neural Computing and Applications, 2023. 35(18): p. 13239-13259.
• Chen, H.-L., et al., A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst. Appl., 2011. 38(7): p. 9014–9022.
• Marcano-Cedeño, A., J. Quintanilla, and D. Andina, WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Systems with Applications, 2011. 38: p. 9573-9579.
• Seera, M. and C.P. Lim, A hybrid intelligent system for medical data classification. Expert Systems with Applications, 2014. 41(5): p. 2239-2249.
• Zheng, B., S.W. Yoon, and S.S. Lam, Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 2014. 41(4, Part 1): p. 1476-1482.
• Jabbar, M.A., Breast Cancer Data Classification Using Ensemble Machine Learning. Engineering and Applied Science Research, 2021. 48(1): p. 65-72.
• Abdel-Zaher, A.M. and A.M. Eldeib, Breast cancer classification using deep belief networks. Expert Systems with Applications, 2016. 46: p. 139-144.
• Kamel, H., D. Abdulah, and J.M. Al-Tuwaijari. Cancer Classification Using Gaussian Naive Bayes Algorithm. in 2019 International Engineering Conference (IEC). 2019.
• Alickovic, E. and A. Subasi. Normalized Neural Networks for Breast Cancer Classification. in CMBEBIH 2019. 2020. Cham: Springer International Publishing.
• Singh, S., et al., Feature Importance Score-Based Functional Link Artificial Neural Networks for Breast Cancer Classification. BioMed Research International, 2022. 2022: p. 2696916.
• Kaur, H., Dense Convolutional Neural Network Based Deep Learning Framework for the Diagnosis of Breast Cancer. Wireless Personal Communications, 2023.
• Pawlovsky, A.P. and H. Matsuhashi. The use of a novel genetic algorithm in component selection for a kNN method for breast cancer prognosis. in 2017 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE). 2017.
• Rajaguru, H. and S. Chakravarthy, Analysis of Decision Tree and K-Nearest Neighbor Algorithm in the Classification of Breast Cancer. Asian Pacific journal of cancer prevention : APJCP, 2019. 20: p. 3777-3781.
• Admassu, T., An optimized K-Nearest Neighbor based breast cancer detection. Journal of Robotics and Control (JRC), 2021. 2.
• Henderi, H., Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer. IJIIS: International Journal of Informatics and Information Systems, 2021. 4: p. 13-20.
• Tounsi, S., I.F. Kallel, and M. Kallel. Breast cancer diagnosis using feature selection techniques. in 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). 2022.
• Priyadarshini, J., et al. Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor. Applied Sciences, 2023. 13(2), 906.