Diagnosis, prediction and big health management platform based on cancer genome big data core algorithm

Abstract

The invention discloses a diagnosis, prediction and big health management platform based on a cancer genome big data core algorithm. The platform comprises a client end, a data end and a big health management platform; a user registers through the client end, and sends gene data to the big health management platform through the client after completing registration; the data end periodically acquires the gene big data and transmits the acquired gene big data to the big health management platform; the big health management platform is used for storing and preprocessing the gene big data and establishing a disease diagnosis model according to the preprocessed gene big data, and the disease diagnosis model performs diagnosis according to the gene data of the user and sends a diagnosis result of the disease diagnosis model to the corresponding client end. According to the platform, the mode of preprocessing the disease big data has relatively high clustering accuracy; and classes obtained by clustering are utilized to train a BP neural network, so that the structure of the gene big data can be effectively simplified, and complexity and overfitting of BP neural network algorithm learning are avoided.

Description

technical field [0001] The invention relates to the field of health management, in particular to a platform for diagnosis, prediction and big health management based on the core algorithm of cancer genome big data. Background technique [0002] Advances in science and technology have promoted rapid reforms in all walks of life, especially in biology. The success of whole gene sequencing has led to a sharp drop in the cost of obtaining cancer gene expression data, providing a broad platform for systematic research on cancer genomes. Based on genetic data, machine learning is used to conduct computer-aided diagnosis on genetic data, which has a high accuracy of lesion diagnosis. However, the characteristics of genetic data are high dimensionality, small sample size, large signal-to-noise ratio, high feature dimensionality, and strong correlation. When machine learning classification algorithms are applied to such genetic data, it is easy to cause training overfitting and dimen...

AI Technical Summary

Problems solved by technology

However, the characteristics of genetic data are high dimensionality, small sample size, large signal-to-noise ratio, high feature dimensionality, and strong correlation. When machine learning classification algorithms are applied to such genetic data, it is easy to cause training overfitting and dimensionality disaster.

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