Abstract: Big data usually comes from different data sources with diverse formats, structures, and qualities. Big data often contains a large number of redundant features, which can affect the accuracy of data classification during feature integration. To address these issues, a deep learning-based algorithm is proposed for feature integration classification in hospital big data. A feature extraction model is established based on deep learning to extract relevant features from the data. However, since the training process of the model introduces a significant amount of noise, the extracted features may contain irrelevant information, which can impact the results of feature integration classification. Therefore, a stacked sparse denoising autoencoder is employed to suppress irrelevant features. The best training parameters are determined using divergence functions and greedy algorithms, and a loss function is utilized to sparsify the irrelevant features in the feature space, resulting in practical data features.A feature integration classification model is constructed using an autoencoder network, and with the assistance of type-constrained functions and objective functions, the optimal integration centers for each class are obtained to achieve data feature integration classification. Experimental results demonstrate that the proposed method exhibits excellent classification performance, with macro-averaged values above 0. 95, and it also shows fast classification speed, indicating its effectiveness in classification.

Key words: deep learning, medical big data, feature integration, stacked sparse noise reduction encoder, integration center