Journal ID : TRKU-15-03-2020-10524
[This article belongs to Volume - 62, Issue - 03]
Total View : 405

Title : Weighted Decision Tree Model for Breast Cancer Detection

Abstract :

Breast cancer detection is one of imbalanced classification problem in machine learning. The breast cancer dataset consists of significantly more class of non-cancerous observations than the cancerous observations. The classification of imbalanced dataset is a problem to machine learning algorithm due to the fact that the standard machine learning algorithms assume the class in the dataset are balanced or equal. The imbalance of the classes in breast cancer dataset makes the detection of breast cancer more difficult with the existing standard machine learning algorithms. This is because the algorithms are biased prediction due to the class imbalance in the dataset. In this research, a solution to imbalanced classification problem is proposed by proposing a weighted decision tree model for breast cancer detection. Finally, the performance of the proposed model is tested and result reveals an accuracy of 94.03% is achieved. Moreover, experimental test on the breast cancer dataset shows that better performance is achieved by the proposed model as compared to the standard decision tree model

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