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Comparative Analysis of Machine Learning Algorithms in Breast Cancer Classification

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Abstract

Now days breast cancer has emerged as a diseases effecting women to suffer a life threating phase and eventually lead to death world wide. The prediction of breast cancer in woman at the initial stage can aggrandize recovery and chance of abidance considerably as the essential medical treatments can be adapted on time and stop its further growth. Moreover the precise categorization of tumor eliminates the avoidable treatments and patients skips from witnessing the medical emergencies. Thus the exact categorization of breast cancer either benign or malignant and the precised analysis of each is a matter of important exploration. Machine learning have extensively beneficial aspects in critical feature extraction from the breast cancer dataset. Thus the machine learning can be astronomically honored as a alternative methodology in breast cancer pattern categorization and forecast modeling. In this paper ML techniques namely Support vector machines, logistic Regression, Random forest tree and k-nearest neighbours (k-Nns) are over viewed and later performance measures compared for breast cancer analysis and prognosis.

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Data and Material Availability

Reference link: UCI Machine Learning Repository.: https://archive.ics.uci.edu/ml/machinelearning-databases/breast-cancer-wisconsin/wdbc.names. Accessed [August] [2017]. B. M. Gayathri and C. P. Sumathi, “Comparative study of relevance vector machine with various machine learning techniques used for detecting breast cancer,” 2016 IEEE Int. Conf. on Computational Intelligence and Computing Research (ICCIC), pp 1–5, IEEE,2016. Morteza Heidari et al. 2017. “Prediction of Breast Cancer Risk using Machine Learning Approach embedded with a Locality Preserving Projection Algorithm.” Institute of Physics in Medicine and Biology (IPEM), https://doi.org/10.1088/1361-6560/aaa1ca. H. Hasan and N. M. Tahir, "Feature selection of breast cancer based on principal component analysis," in Signal Processing and Its Applications (CSPA), 2010 6th Internationa Colloquium on, 2010, pp. 1–4. B. Nithya, V. Ilango, 2017, “Comparative Analysis of Classification Methods in R Environment with two Different Datasets.”, Intl J Scientific Research and Computer Science, Engineering and Information Technology (IJSRCSEIT), vol 2, Issue 6, ISSN: 2456- 3307.

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On request and provided by corresponding author.

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Authors and Affiliations

  1. Department of Computer Science and Application, Dr. Harisingh Gour University, Sagar, Madhya Pradesh, India

    Satish Chaurasiya & Ranjit Rajak

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  1. Satish Chaurasiya
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  2. Ranjit Rajak
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Correspondence to Ranjit Rajak.

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Chaurasiya, S., Rajak, R. Comparative Analysis of Machine Learning Algorithms in Breast Cancer Classification. Wireless Pers Commun 131, 763–772 (2023). https://doi.org/10.1007/s11277-023-10438-9

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