Breast Cancer Prediction Using a Hybrid Data Mining Model

Elham Bahmani - Islamic Azad University, Malayer, Iran
Mojtaba Jamshidi - Islamic Azad University, Qazvin, Iran
Abdusalam Shaltooki - University of Human Development, Sulaymaniyah, Iraq

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Today, with the emergence of data mining technology and access to useful data, valuable information in different areas can be explored. Data mining uses machine learning algorithms to extract useful relationships and knowledge from a large amount of data and offers an automatic tool for various predictions and classifications. One of the most common applications of data mining in medicine and health-care is to predict different types of breast cancer which has attracted the attention of many scientists. In this paper, a hybrid model employing three algorithms of Naive Bayes Network, RBF Network, and K-means clustering is presented to predict breast cancer type. In the proposed model, the voting approach is used to combine the results obtained from the above three algorithms. Dataset used in this study is called Breast Cancer Wisconsin taken from data sources of UCI. The proposed model is implemented in MATLAB and its efficiency in predicting breast cancer type is evaluated on Breast Cancer Wisconsin dataset. Results show that the proposed hybrid model achieves an accuracy of 99% and mean absolute error of 0.019 which is superior over other models.


Data mining, breast cancer, hybrid model, RBF network, Naive Bayes, K-means

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