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Open Access 01-12-2024 | Malaria | Research

Risk assessment of imported malaria in China: a machine learning perspective

Authors: Shuo Yang, Ruo-yang Li, Shu-ning Yan, Han-yin Yang, Zi-you Cao, Li Zhang, Jing-bo Xue, Zhi-gui Xia, Shang Xia, Bin Zheng

Published in: BMC Public Health | Issue 1/2024

Abstract

Background

Following China’s official designation as malaria-free country by WHO, the imported malaria has emerged as a significant determinant impacting the malaria reestablishment within China. The objective of this study is to explore the application prospects of machine learning algorithms in imported malaria risk assessment of China.

Methods

The data of imported malaria cases in China from 2011 to 2019 was provided by China CDC; historical epidemic data of malaria endemic country was obtained from World Malaria Report, and the other data used in this study are open access data. All the data processing and model construction based on R, and map visualization used ArcGIS software.

Results

A total of 27,088 malaria cases imported into China from 85 countries between 2011 and 2019. After data preprocessing and classification, clean dataset has 765 rows (85 * 9) and 11 cols. Six machine learning models was constructed based on the training set, and Random Forest model demonstrated the best performance in model evaluation. According to RF, the highest feature importance were the number of malaria deaths and Indigenous malaria cases. The RF model demonstrated high accuracy in forecasting risk for the year 2019, achieving commendable accuracy rate of 95.3%. This result aligns well with the observed outcomes, indicating the model’s reliability in predicting risk levels.

Conclusions

Machine learning algorithms have reliable application prospects in risk assessment of imported malaria in China. This study provides a new methodological reference for the risk assessment and control strategies adjusting of imported malaria in China.

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Title: Risk assessment of imported malaria in China: a machine learning perspective
Authors: Shuo Yang
Ruo-yang Li
Shu-ning Yan
Han-yin Yang
Zi-you Cao
Li Zhang
Jing-bo Xue
Zhi-gui Xia
Shang Xia
Bin Zheng
Publication date: 01-12-2024
Publisher: BioMed Central
Keyword: Malaria
Published in: BMC Public Health / Issue 1/2024
Electronic ISSN: 1471-2458
DOI: https://doi.org/10.1186/s12889-024-17929-9

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Springer Medicine

Risk assessment of imported malaria in China: a machine learning perspective

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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