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.

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Heart disease is the leading cause of death in Indonesia based on 2010 Hospital Information System (SIRS) Report. Early detection and treatment of heart disease will reduce the patient mortality rate. Therefore, implementation of artificial neural networks (ANN) technique in diagnosing heart disease have been widely used and reached good accuracy. Beside of that, there are disadvantages in implementation of ANN technique, such as a long training process, many parameters have to be tuned, the obtained solution potentially get stuck in local minima, and activation function must be differentiable. We implemented Extreme Learning Machine (ELM) which is fast, simple tuning, and better generalization model learning algorithm. It has better performance than backpropagation ANN, Support Vector Machine (SVM), and decision tree. The results indicate that the ELM model has potentially implemented to help medical professional in diagnosing heart disease.