Breast Cancer Detection Using Machine Learning Algorithms

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Shastri R.; Pradeep N.; Rao Mangalore K.K.; Rajpal B.; PRASAD N.

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Journal: International Journal of Research in Industrial Engineering Year:2020 | Volume:9 | Issue:3 Page(s): 235-246

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Title

Breast Cancer Detection Using Machine Learning Algorithms

Author(s)

Keywords

Machine learning

K-nearest neighbors

Support vector machine

Decision tree classifier

Jupyter

Abstract

Breast cancer has been the riskiest malignancy among ladies around the world. Nearly 2 million new cases were diagnosed in 2018. The main problem in the detection of breast cancer is to find how tumors turn into malignant or benign and we can do this with the help of Machine learning techniques as they provide an appropriate result. According to research, an experienced physician can diagnose cancer with 79% accuracy while using Machine learning techniques provides an accuracy of 91%. In this work, Machine learning techniques have been applied which include K-nearest neighbors algorithm (KNN), Support vector machine (SVM), and Decision tree classifier (DT). To predict whether the cause is benign or malignant we have used the breast cancer dataset. The SVM classifier gives more accurate and precise results as compared to others, and this classifier is trained with the larger datasets.

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APA: Copy

Shastri, R., Pradeep, N., Rao Mangalore, K.K., Rajpal, B., & PRASAD, N.. (2020). Breast Cancer Detection Using Machine Learning Algorithms. INTERNATIONAL JOURNAL OF RESEARCH IN INDUSTRIAL ENGINEERING, 9(3), 235-246. SID. https://sid.ir/paper/770859/en

Vancouver: Copy

Shastri R., Pradeep N., Rao Mangalore K.K., Rajpal B., PRASAD N.. Breast Cancer Detection Using Machine Learning Algorithms. INTERNATIONAL JOURNAL OF RESEARCH IN INDUSTRIAL ENGINEERING[Internet]. 2020;9(3):235-246. Available from: https://sid.ir/paper/770859/en

IEEE: Copy

R. Shastri, N. Pradeep, K.K. Rao Mangalore, B. Rajpal, and N. PRASAD, “Breast Cancer Detection Using Machine Learning Algorithms,” INTERNATIONAL JOURNAL OF RESEARCH IN INDUSTRIAL ENGINEERING, vol. 9, no. 3, pp. 235–246, 2020, [Online]. Available: https://sid.ir/paper/770859/en

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