A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination Model

Antibiotics

Jianping Li¹  Yongjun Zheng¹  Ding Xu¹  Xinxing Li²  Ziyi Zhang²  Congming Wu³ 

[1] Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Engineering, China Agricultural University, Beijing 100083, China;Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;College of Veterinary Medicine, China Agricultural University, Beijing 100083, China;

关键词: drug resistance;    microbial;    BP neural network;    grey system;    GM(1,1)-BP neural network model;   

DOI  :  10.3390/antibiotics10060692

来源: DOAJ

【 摘 要 】

There is an increasing drug resistance of animal-derived pathogens, seriously posing a huge threat to the health of animals and humans. Traditional drug resistance testing methods are expensive, have low efficiency, and are time-consuming, making it difficult to evaluate overall drug resistance. To develop a better approach to detect drug resistance, a small sample of Escherichia coli resistance data from 2003 to 2014 in Chengdu, Sichuan Province was used, and multiple regression interpolation was applied to impute missing data based on the time series. Next, cluster analysis was used to classify anti-E. coli drugs. According to the classification results, a GM(1,1)-BP model was selected to analyze the changes in the drug resistance of E. coli, and a drug resistance prediction system was constructed based on the GM(1,1)-BP Neural Network model. The GM(1,1)-BP Neural Network model showed a good prediction effect using a small sample of drug resistance data, with a determination coefficient R² of 0.7830 and an RMSE of only 0.0527. This model can be applied for the prediction of drug resistance trends of other animal-derived pathogenic bacteria, and provides the scientific and technical means for the effective assessment of bacterial resistance.

【 授权许可】

Unknown