Analysis of Different Machine Learning Techniques with PCA in the Diagnosis of Breast Cancer

Year 2022, Volume: 7 Issue: 3, 195 - 205, 30.12.2022

Hüseyin Yılmaz , Fatma Kuncan

https://doi.org/10.30931/jetas.1166768

Abstract

In recent years, different types of cancer cases are common. Increasing cancer cases, A rapidly increasing health for countries and humanity becomes a problem. In addition to being the most common cancer among women today, breast cancer has surpassed lung cancer as the most common cancer type in the world since 2021. Early diagnosis greatly reduces the risk of death in breast cancer, and benign tumors are correctly diagnosed, allows the classification of this field to be a new research topic. New developments in the field of Medicine and Technology Machine learning, classification algorithms and computerized diagnosis are used in the correct classification of tumors. increased its use. These systems are extremely important in terms of being an assistant to the expert opinion. In this study, in the Wisconsin Breast Cancer dataset, it is aimed to accelerate the diagnosis of the disease and to reduce the tumors, different machine learning to minimize treatment processes by providing accurate classification techniques were used. In this study, we reduced our dataset to 171 data using Principal Component Analysis (PCA) to accelerate disease diagnosis on the Wisconsin Breast Cancer dataset and 2 different classification processes were performed using 5 different machine learning. The success rate of each algorithm was compared, and it was revealed that Logistic Regression was the most successful method with an accuracy rate of 98.8% after PCA.

Keywords

Breast cancer, PCA, classification algorithms, machine learning, logistic regression

Thanks

This article study was carried out in Siirt University Engineering Faculty Human Computer Interaction Laboratory. I would like to thank the Human Computer Interaction Laboratory staff for their support.

References

• [1] Choi, Y.K., Woo, S.M., Cho, S.G., Moon, H.E., Yun, Y.J., Kim, J.W., Ko, S.G., "Brain-metastatic triple-negative breast cancer cells regain growth ability by altering gene expression patterns", Cancer Genomics & Proteomics 10(6) (2013) : 265-275.
• [2] Waks, A.G., Winer, E.P., "Breast cancer treatment: a review", Jama 321(3) (2019) : 288-300.
• [3] Yancik, R., Ries, L.A., "Aging and cancer in America: demographic and epidemiologic perspectives", Hematology/oncology clinics of North America 14(1) (2000) : 17-23.
• [4] Goldstein, A.J., Harmon, L.D., Lesk, A.B., "Identification of human faces", Proceedings of the IEEE 59(5) (1971) : 748-760.
• [5] Agarap, A.F.M., "On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset", In Proceedings of the 2nd international conference on machine learning and soft computing (2018) : 5-9.
• [6] Toğaçar, M., Ergen, B., "Deep learning approach for classification of breast cancer", In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (2018) : 1-5. IEEE.
• [7] Yavuz, E., Eyüpoğlu, C., "Meme Kanseri Teşhisi İçin Yeni Bir Skor Füzyon Yaklaşımı", Düzce Üniversitesi Bilim ve Teknoloji Dergisi 7(3) (2019) : 1045-1060.
• [8] Bayrak, E.A., Kırcı, P., Ensari, T., Seven, E., "Dağtekin, M., Göğüs Kanseri Verileri Üzerinde Makine Öğrenmesi Yöntemlerinin Uygulanması", Journal of Intelligent Systems: Theory and Applications 5(1) (2022) : 35-41.
• [9] Ganggayah, M.D., Taib, N.A., Har, Y.C., Lio, P., Dhillon, S.K., "Predicting factors for survival of breast cancer patients using machine learning techniques", BMC medical informatics and decision making 19(1) (2019) : 1-17.
• [10] Singh, S., Jangir, S.K., Kumar, M., Verma, M., Kumar, S., Walia, T.S., Kamal, S.M., "Feature Importance Score-Based Functional Link Artificial Neural Networks for Breast Cancer Classification", BioMed Research International (2022) : 1-8.
• [11] Ghosh, P., "Breast Cancer Wisconsin (Diagnostic) Prediction", Available online: https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic) (accessed on 1 October 2022).
• [12] Mangukiya, M., Vaghani, A., Savani, M., "Breast Cancer Detection with Machine Learning", International Journal for Research in Applied Science and Engineering Technology 10(2) (2022) : 141-145.
• [13] Argun, İ.D., Nalbant, B., "Using Classification Algorithms in Data Mining in Diagnosing Breast Cancer", Advances in Artificial Intelligence Research 2(2) (2022) : 65-70.
• [14] Bayrak, E.A., Kırcı, P., Ensari, T., Seven, E., Dağtekin, M., "Göğüs Kanseri Verileri Üzerinde Makine Öğrenmesi Yöntemlerinin Uygulanması", Journal of Intelligent Systems: Theory and Applications 5(1) (2022) : 35-41.
• [15] Jolliffe, I.T., Cadima, J., "Principal component analysis: a review and recent developments", Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374(2065) (2016) : 20150202.
• [16] Ringnér, M., "What is principal component analysis?", Nature biotechnology 26(3) (2008) : 303-304.
• [17] Ding, C., Zhou, D., He, X., Zha, H., "R 1-pca: rotational invariant l 1-norm principal component analysis for robust subspace factorization", In Proceedings of the 23rd international conference on Machine learning (2006) : 281-288.
• [18] Schapire, R.E., "Explaining adaboost", In Empirical inference (2013) : 37-52. Springer, Berlin, Heidelberg.
• [19] Wang, R., "AdaBoost for feature selection, classification, and its relation with SVM, a review", Physics Procedia 25 (2012) : 800-807.
• [20] Gao, L., Cheng, W., Zhang, J., Wang, J., "EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine", Review of scientific instruments 87(8) (2016) : 085110.
• [21] Quinlan, J.R., "Learning decision tree classifiers", ACM Computing Surveys (CSUR) 28(1) (1996) : 71-72.
• [22] Myles, A.J., Feudale, R.N., Liu, Y., Woody, N.A., Brown, S.D., "An introduction to decision tree modeling", Journal of Chemometrics: A Journal of the Chemometrics Society 18(6) (2004) : 275-285.
• [23] Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K., "KNN model-based approach in classification", In OTM Confederated International Conferences On the Move to Meaningful Internet Systems (2003) : 986-996. Springer, Berlin, Heidelberg.
• [24] Sha’Abani, M.N.A.H., Fuad, N., Jamal, N., Ismail, M.F., "kNN and SVM classification for EEG: a review", InECCE2019 (2020) : 555-565.
• [25] Biau, G., Scornet, E., "A random forest guided tour", Test 25(2) (2016) : 197-227.
• [26] More, A.S., Rana, D.P., "Review of random forest classification techniques to resolve data imbalance", In 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM) (2017) : 72-78. IEEE.
• [27] Wright, R.E., "Logistic regression", (1995) : 217-244.