International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Design and Development of Cancer Prediction using Machine Learning Technology System

Abstract

Breast cancer is one of the utmost shared disease in women, classifying and predicting it is a vibrant research issue. Various machine learning system have been utilized to create different cancer models. Among various algorithms, Support Vector Machines and k nearest neighbors have been appeared to outnumber other algorithms. Though there are few studies concentrated on examining the performance of different classification algorithms .The motive of this paper is to evaluate the performance of SVM and KNN on breast cancer dataset. The cancer dataset (Wisconsin Dataset) is taken from UCI machine Repository, place for machine learning and insight Framework. The precision, accuracy F-measures of different classification algorithms are looked at. The outcome shows that SVM classifier can give the better result for classification, while accuracy of the algorithm is improved by modifying the attributes of the dataset.

Country : India

1 Ashish Singh2 Vishal Tripathi3 Ankit Singh4 Shiwani Gupta

  1. Computer Science and Engineering Department, Thakur College of Engineering and Technology, Mumbai, India
  2. Computer Science and Engineering Department, Thakur College of Engineering and Technology, Mumbai, India
  3. Computer Science and Engineering Department, Thakur College of Engineering and Technology, Mumbai, India
  4. Assistant Professor, Computer Science and Engineering Department, Thakur College of Engineering and Technology, Mumbai, India

IRJIET, Volume 3, Issue 3, March 2019 pp. 14-17

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References

  1. USA Cancer Statistics Working Group. THE United States Cancer Statistics in 1999–2008 Incidence and Mortality Web-base.
  2. V. Chaurasia and S. Pal Data Minings : To Predict and to Resolve Breast Cancer Survivability vol. 3,2014.
  3. A.C.Y, “An Empirical Comparison of Data Mining Classification Methods”.
  4. F.Paulin et al. Jan 2011., Classification of Breast cancer by comparing Back propagation training algorithms‖, International Journal of Computer Sciences and Engineering, Vol 3, 327 – 332,
  5. P.Dhivyapriya and Dr.S.Sivakumar, Jan-feb 2017Classification of Cancer Dataset in Data Mining Algorithms Using R Tool‖, International Journal of Computer Science Trends and Technology (IJCST), Vol.5, Issue 1.
  6. Dubey, A.K., Gupta, U. & Jain, S, November 2016 Analysis of k-means clustering approach on the breast cancer Wisconsin dataset, International Journal of Computer Assisted Radiology and Surgery.
  7. Borges and Lucas Rodrigues, Analysis of Wisconsin Breast Cancer Dataset and Machine Learning for Breast Cancer Detection‖, Proceedings Computational, October, 2015.

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