Comparative study of machine learning techniques for breast cancer identification/diagnosis

G. Ganapathy, N. Sivakumaran, Murugesan Punniyamoorthy, R. Surendheran and Srijan Thokala

International Journal of Enterprise Network Management, 2019, vol. 10, issue 1, 44-63

Abstract: The number of new cases of female breast cancer was 124.9 per 100,000 women per year. Similarly, deaths were 21.2 per 100,000 women per year. It calls for an urge to increase the awareness of breast cancer and very accurately analyse the causes which may differ in minute variations. This is why the application of computation techniques are widely increasing to support the diagnostic results. In this paper, we present the application of several machine learning techniques and models like neural network, SVM is used to quantify the classifications. The techniques that are most reliable, accurate and robust are emphasised. It gives a plethora of explorations into the research field for developing predictive models. To achieve higher reliability on the data, we present the comparison of various Machine Learning techniques on a dataset that is available on the website Kaggle.

Keywords: breast cancer; machine learning; neural network; FNA; SVM; kernel; KNN; naive Bayes. (search for similar items in EconPapers)
Date: 2019
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