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With the development of computer technology, the electronic of medical data has become a reality. Now, how to analyze the data sufficiently to predict patient's disease and conduct early intervention has become a focused research direction. The patient's intuitive expression of feelings is also an aspect that cannot be ignored. Doctors record the pathological characteristics of patients in system. In the paper, we proposed a sentence similarity model to carry out symptom similarity analysis to achieve elementary disease prediction and early intervention, which makes use of word embedding and convolutional neural network (CNN) to extract a sentence vector that contains keyword information about the patient's feelings and symptoms. In order to increase the accuracy of sentence similarity computation, this model integrated syntactic tree and neural network into the computation process. Our main innovation is to use symptom similarity analysis model for disease prediction and early intervention. In addition, the SPO kernel is also one of the innovations. Finally, the results of experiment on Microsoft research paraphrase identification (MSRP) indicated that our model can achieve an excellent performance reached 83.9% in the terms of F1 and accuracy. Furthermore, we also conducted experiments on the data of the Semantic Textual Similarity task. Pearson correlation coefficient indicates that our result is closer to the gold standard scores, which illustrates that it can extract the key information of sentence well to realize the prediction of disease and carry out early intervention.INDEX TERMS Convolutional neural network, disease predictions, early intervention, symptom similarity analysis.

Sentence similarity analysis has been applied in many fields, such as machine translation, the question answering system, and voice customer service. As a basic task of natural language processing, sentence similarity analysis plays an important role in many fields. The task of sentence similarity analysis is to establish a sentence similarity scoring model through multi-features. In previous work, researchers proposed a variety of models to deal with the calculation of sentence similarity. But these models do not consider the association information of sentence pairs, but only input sentence pairs into the model. In this article, we propose a sentence feature extraction model based on multi-feature attention. In addition, with the development of deep learning and the application of nature-inspired algorithms, researchers have proposed various hybrid algorithms that combine nature-inspired algorithms with neural networks. The hybrid algorithms not only solve the problem of decision-making based on multiple features but also improve the performance of the model. In the model, we use the attention mechanism to extract sentence features and assign weight. Then, the convolutional neural network is used to reduce the dimension of the matrix. In the training process, we integrate the firefly algorithm in the neural networks. The experimental results show that the accuracy of our model is 74.21%.

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