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Published January 16, 2020 | Version v1
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An Ensemble Classifier Approach for Diagnosis of Breast Cancer

Creators

  • 1. Assistant Professor, Department of Computer Science and Engineering, Dayananda Sagar College of Engineering, VTU University, Bangalore, Karnataka, India
  • 2. Professor, Department of Computer Science and Engineering, Dayananda Sagar College of Engineering, VTU University, Bangalore, Karnataka India

Description

Accurate and early diagnosis of breast cancer increases survival rate of patients. Diagnosis of Breast cancer involves identifying tumour as either benign or malignant. In this paper, proposed methodology is an integration of ensemble classifiers AdaBoost and Random Forest named as ADARF a prediction model for diagnosis of breast cancer. The main objective is to enhance the performance and to reduce error. Experimental result shows that the proposed approach has higher accuracy of 98.8% compared to Logistic Regression (LR), K Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifiers.  

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Additional details

References

  • Cancer Research UK, [Online] Available from: https://www.cancerresearchuk.org/about-cancer/cancer-symptoms/why-is-early-diagnosis-important
  • Pei-Chann Chang, Chen-Hao Liu, Chin-Yuan Fan, Jun-Lin Lin, Chih-Ming Lai (16–19 September, 2009), "An ensemble of neural networks for stock trading decision making", International Conference on Intelligent Computing, Ulsan, South Korea
  • M. Lichman, UCI Machine Learning Repository (2013), [Online] Available from: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin
  • Md. Milon Islam, Hasib Iqbal, Md. Rezwanul Haque, Md. Kamrul Hasan (21–23 December, 2017), "Prediction of breast cancer using support vector machine and K-nearest neighbours", IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, Bangladesh
  • Reem Alyami, Jinnan Alhajjaj, Batool Alnajrani, Ilham Elaalami, Abdullah Alqahtani, Nahier Aldhafferi, Sunday O. Olatunji (21–23 February, 2017), "Investigating the effect of Correlation based feature selection on breast cancer diagnosis using artificial neural network and support vector machines", International Conference on Informatics, Health & Technology (ICIHT), Riyadh, Saudi Arabia

Subjects

Computer Science / IT Journals
http://matjournals.com/Engineering-Journals.html