Purpose

We present a Health Care System (HCS) based on integrated learning to achieve high-efficiency and high-precision integration of medical and health big data, and compared it with an internet-based integrated system.

Method

The method proposed in this paper adopts the Bagging integrated learning method and the Extreme Learning Machine (ELM) prediction model to obtain a high-precision strong learning model. In order to verify the integration efficiency of the system, we compare it with the Internet-based health big data integration system in terms of integration volume, integration efficiency, and storage space capacity.

Results

The HCS based on integrated learning relies on the Internet in terms of integration volume, integration efficiency, and storage space capacity. The amount of integration is proportional to the time and the integration time is between 170-450 ms, which is only half of the comparison system; whereby the storage space capacity reaches 8.3×2⁸TB.

Conclusion

The experimental results show that the integrated learning-based HCS integrates medical and health big data with high integration volume and integration efficiency, and has high space storage capacity and concurrent data processing performance.

中文翻译：

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基于综合学习型医疗保健系统的医疗卫生大数据管理：回顾与比较分析

目的

我们提出了一种基于集成学习的医疗保健系统（HCS），以实现医疗卫生大数据的高效、高精度集成，并将其与基于互联网的集成系统进行比较。

方法

本文提出的方法采用 Bagging 集成学习方法和极限学习机 (ELM) 预测模型，得到了一个高精度的强学习模型。为了验证系统的集成效率，我们将其与基于互联网的健康大数据集成系统在集成量、集成效率、存储空间容量等方面进行了比较。

结果

基于集成学习的HCS在集成量、集成效率、存储空间容量等方面都依赖于互联网。积分量与时间成正比，积分时间在170-450ms之间，仅为比较系统的一半；从而存储空间容量达到8.3×2 ⁸ TB。

结论

实验结果表明，基于集成学习的HCS集成了医疗健康大数据，具有较高的集成量和集成效率，具有较高的空间存储容量和并发数据处理性能。