Intelligent medical heterogeneous big data set balanced clustering using deep learning

Abstract

Abstract In order to address the clustering problem of intelligent medical data, the data sets were not preprocessed using the traditional method, leading to a large amount of calculation, low efficiency, and large data cluster center offset distance. We proposed a balanced clustering algorithm for intelligent medical heterogeneous big data set using deep learning. Firstly, a deep neural network model based on incremental updating was constructed, and adaptive training and adjustment were made according to data scale, and the multi-layer feature learning of heterogeneous big data sets of intelligent medical care. Secondly, under-sampling preprocessing was carried out on the data set so that the data of the heterogeneous big data set was in a balanced state, and on this basis, clustering calculation of the heterogeneous big data was conducted. Then, the clustering center was set according to the kernel density estimation results, and the data cluster center was updated iteratively until convergence by combining the data features obtained from deep learning and euclidean distance calculation, so as to complete the balanced clustering of the heterogeneous big data set of intelligent medical treatment. The results show that the proposed algorithm has the advantages of small data cluster center offset distance, short clustering time, low energy consumption, high Macro-F1 value and NMI value, and the accuracy of clustering can be as high as 95%, the calculational cost is low, which has certain advantages. 2020 Elsevier Ltd. All rights reserved.