E3S Web Conf.
Volume 185, 20202020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
|Number of page(s)||4|
|Section||Medical Biology and Medical Signal Processing|
|Published online||01 September 2020|
Research on diabetes prediction method based on electronic medical record data analysis
1 Zhejiang University, Hangzhou, 310058, China
2 Sichuan University, Chengdu, 610065, China
3 Dongguan University of Technology, Dongguan, 523808, China
4 Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
5 Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
6 Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
* Corresponding author: firstname.lastname@example.org
The continuous progress of computer science and technology has accelerated the pace of informatization construction of the medical system. Medical technology has developed rapidly in various research directions, and the construction of medical IT systems has been continuously improved. The popular application of electronic medical records has produced massive medical data in the medical process. At the same time, in medical behavior, more and more rely on data to make relevant judgments. The coverage of medical equipment is becoming more and more extensive, and the accuracy of data is constantly improving, and the clinical diagnosis is gradually shifting from qualitative judgment to quantitative analysis. Based on the analysis of electronic medical record data, this article studies and analyzes the risk factors leading to diabetes. By analyzing the characteristic variables, the risk factors significantly related to diabetes are obtained as the input variables of the BP neural network model. For complex problems, machine learning algorithms have higher accuracy and stronger generalization capabilities. Based on the BP artificial neural network model, this paper builds and builds a machine learning simulation to predict diabetes.
© The Authors, published by EDP Sciences, 2020
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