The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis
Open accessAbstract
Breast cancer represents one of the diseases that make a high number of deaths every year. It is the most common type of all cancers and the main cause of women's deaths worldwide. Classification and data mining methods are an effective way to classify data. Especially in medical field, where those methods are widely used in diagnosis and analysis to make decisions. In this paper, a performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB) and k Nearest Neighbors (k-NN) on the Wisconsin Breast Cancer (original) datasets is conducted. The main objective is to assess the correctness in classifying data with respect to efficiency and effectiveness of each algorithm in terms of accuracy, precision, sensitivity and specificity. Experimental results show that SVM gives the highest accuracy (97.13%) with lowest error rate. All experiments are executed within a simulation environment and conducted in WEKA data mining tool.
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Cites
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Top 10 algorithms in data mining
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What is a support vector machine?
Noble, W. S. (2006) · Nature Biotechnology · doi:10.1038/nbt1206-1565 · Cited by 4,301
An empirical study of the naive Bayes classifier
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Transductive Inference for Text Classification using Support Vector Machines
Joachims, T. (1999) · Cited by 2,723
Predicting breast cancer survivability: a comparison of three data mining methods
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Discovering Knowledge in Data: An Introduction to Data Mining
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Data Mining Techniques: To Predict and Resolve Breast Cancer SurvivabilityPreprint
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Decision Tree Based Predictive Models for Breast Cancer Survivability on Imbalanced Data
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Big data in healthcare: Challenges and opportunities
Asri, H., Mousannif, H., Moatassime, H. A., & Noël, T. (2015) · International Conference on Cloud Computing Technologies and Applications (CloudTech) · doi:10.1109/cloudtech.2015.7337020 · Cited by 101
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Cited by
Machine-learning-based disease diagnosis: a comprehensive review
Ahsan, M. M., Luna, S. A., & Siddique, Z. (2022) · Healthcare · doi:10.3390/healthcare10030541 · Cited by 603
Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
din, N. M. U., Dar, R. A., Rasool, M., & Assad, A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106073 · Cited by 363
Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning
Abunasser, B., AL-Hiealy, M. R., Zaqout, I., & Abu-Naser, S. (2023) · Asian Pacific Journal of Cancer Prevention · doi:10.31557/apjcp.2023.24.2.531 · Cited by 174
Yu, D., Liu, Z., Su, C., Han, Y., Duan, X., Zhang, R., Liu, X., Yang, Y., & Xu, S. (2020) · Thoracic Cancer · doi:10.1111/1759-7714.13204 · Cited by 80
Computational Approaches in Theranostics: Mining and Predicting Cancer Data
Cova, T. F. G. G., Bento, D. J., & Nunes, S. C. C. (2019) · Pharmaceutics · doi:10.3390/pharmaceutics11030119 · Cited by 32
Metadata
- Authors
- Asri, H., Mousannif, H., Moatassime, H. A., & Noel, T.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2016
- Volume
- 83
- Pages
- 1064-1069
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2370924594
- Cited by
- 772
- References
- 11
- FWCI
- 35.21
- Type
- conference paper
- Language
- en
- Open access
- diamond
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