Breast Cancer Prediction Using the Affinity Propagation Clustering with Regard to the Weights of Variables

By using data mining tools in the field of medical diagnosis, some limitations such as the high cost of some tests or their timing will be addressed. In addition, the existence of errors in some experiments has led researchers to be welcomed by categorization methods. In this regard, the present study, based on the combination of clustering and categorization methods, has proposed a new method for the diagnosis of breast cancer. In this operation, the combination is performed using an iterative algorithm and a dependency propagation clustering algorithm. This method produces weights for variables using an innovative algorithm and forms cluster clusters based on the dependency propagation algorithm. Then the number of clusters as a new variable is added to the data, and in the next step, the block algorithm is implemented on the modified dataset containing the main data and the number of clusters. According to the accuracy index, the weights production continues to reach the highest possible precision. According to the numerical experiments conducted in this study, the combination of the dependency emission clustering algorithm with an average accuracy of 36.98 was the most accurate. In addition, the Wilcoxon assumption test confirmed the superiority of the combined neural network compared to other methods.