Efficient Role of Machine Learning Classifiers in the Prediction and Detection of Breast Cancer

9 Pages Posted: 27 Feb 2020

See all articles by Bibhuprasad Sahu

Bibhuprasad Sahu

North Orissa University; Gandhi institute for Technoogy

Amrutanshu Panigrahi

Gandhi Institute for Technology (GIFT)

Date Written: February 27, 2020


The major disease in society is Breast Cancer in women worldwide and 27% of women are affected in cancer. Machine learning classifier is suitable for the physicians to make perfect diagnosis with a low cost and time. Comparative performance analysis of classifier in needed to achieve accurate diagnosis as the medical data consist of high dimension data which are noisy in nature. In this study different machine learning techniques of classifiers are applied to Breast cancer datasets. The cancer rates increase in India that increase in the early thirties but its reach at a peak point at ages 50-64 years. As per NICPR report among 28 women there is a woman who affected breast cancer disease. But this ration will change in urban population, one woman is affected among 22 women. Where in rural this ration is increase as 1 in 60 women? The cure rate of the patient may improve by early diagnosis and treatment which may increase prolonging their lives. Here we have developed a model to identify a cancer cell is benign or malignant; we have used machine learning technique classifiers. Here we have to identify a suitable technique for predicting the disease under different operational conditions and datasets. The result analysis shows that SVM finds out to be a suitable option for identifying different performance matrices such as Sensitivity, Accuracy, error, and Specificity.

Keywords: Machine learning, Breast cancer, Classifiers

Suggested Citation

Sahu, Bibhuprasad and Sahu, Bibhuprasad and Panigrahi, Amrutanshu, Efficient Role of Machine Learning Classifiers in the Prediction and Detection of Breast Cancer (February 27, 2020). 5th International Conference on Next Generation Computing Technologies (NGCT-2019), Available at SSRN: https://ssrn.com/abstract=3545096 or http://dx.doi.org/10.2139/ssrn.3545096

Bibhuprasad Sahu (Contact Author)

North Orissa University ( email )

khurdha, IN Odisha

HOME PAGE: http://www.gift.edu.in

Gandhi institute for Technoogy ( email )


Amrutanshu Panigrahi

Gandhi Institute for Technology (GIFT) ( email )


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