Tumor prognosis prediction system based on deep belief network

Abstract

The invention discloses a tumor prognosis prediction system based on a deep belief network. The system comprises a data collecting module, a data preprocessing module, a data learning prediction module and a prediction result display module, wherein the data collection module is used for collecting tumor information; the data preprocessing module is used for carrying out missing value processing and normalization processing on tumor original data; the data learning prediction module is used for carrying out deep learning and prediction modeling on the tumor data; and the prediction result display module is used for displaying a relative risk output by the data learning prediction module. A Gaussian restricted Boltzmann machine is used to keep the nonlinear characteristics of the data; according to the dimension of input data, the amount of output categories and the accuracy of a model, the deep belief network can be flexibly expanded; and in a model training process, no restriction and hypothesis is adopted, an influence way of a variable for the result and a mutual function among variables can be fully mined, the influence way of different factors for tumor prognosis can be comprehensively revealed, and tumor prognosis prediction accuracy is improved.

Description

technical field [0001] The present invention relates to a tumor prediction system, in particular to a tumor prognosis prediction system based on a deep belief network Background technique [0002] The morbidity and mortality of cancer are high, and it has become the main cause of human death due to disease. With the growth of population and the development of population aging, the burden of disease caused by cancer is further increasing, becoming a large part of current medical expenses. Tumor prognosis prediction analysis can provide clinicians with prognostic information for disease treatment, help formulate treatment plans, improve disease cure rate, improve patient prognosis and quality of life, and effectively reduce disease burden, which is of great significance for cancer control and treatment. The TNM staging system based on tumor invasion depth, lymph nodes, and distant metastasis introduced by the American Cancer Federation has been widely used in cancer clinical ...

AI Technical Summary

Problems solved by technology

[0003] Censored data are common in tumor prognosis data. Censored data are not missing data, but incomplete data that can only provide prognosis information from the starting point to the censored time, and cannot provide complete information from the starting point to the occurrence of events.

The existing neural network-based tumor prognosis prediction analysis methods, or cannot make full use of censored data; or in the case of making full use of censored data, cannot effectively solve the time dependence and nonlinear problems of prognostic factors; or the obtained survival curve Not monotonic; or the constructed neural network is not scalable, which is not conducive to large-scale processing of massive data

[0004] Deep learning is a hot field of current machine learning research. Because of its autonomous feature learning ability and high accuracy, it has been applied in many fields, including speech recognition, image processing, natural language processing, and character portraits. However, deep learning is still rare. Applied to the field of tumor prognosis prediction analysis

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