Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis

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 t...

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Bibliographic Details
Published in:Procedia computer science 2016, Vol.83, p.1064-1069
Main Authors: Asri, Hiba, Mousannif, Hajar, Moatassime, Hassan Al, Noel, Thomas
Format: Article
Language:English
Subjects:
Breast cancer
C4.5
Classification
Effectiveness
Efficiency
k-NN
SVM
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Summary: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2016.04.224