Abstract
Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks (BP ANN) has some limitations. There are parameters to be set in the beginning, long time for training process, and possibility to be trapped in local minima. In this research, we implemented ANN with extreme learning techniques for diagnosing breast cancer based on Breast Cancer Wisconsin Dataset. Results showed that Extreme Learning Machine Neural Networks (ELM ANN) has better generalization classifier model than BP ANN. The development of this technique is promising as intelligent component in medical decision support systems.
Highlights
The out of control development of cells in an organ is called tumors that can be cancerous
We revealed the implementation of artificial neural networks with extreme learning techniques in breast cancer diagnosis
The performances were compared against support vector machines, learning vector quantization, decision tree induction, and other methods based on two-breast cancer data set, sufficient and insufficient data
Summary
The out of control development of cells in an organ is called tumors that can be cancerous. A lot of research showed that ANN delivered good accuracy in breast cancer diagnosis. ANN has some parameters to be tuned in the beginning of training process such as number of hidden layer and hidden nodes, learning rates, and activation function. It takes long time for training process due to complex architecture and parameters update process in each iteration that need expensive computational cost. We revealed the implementation of artificial neural networks with extreme learning techniques in breast cancer diagnosis. Brief review of previous works in breast cancer diagnosis are presented.
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