Application of machine learning models in schistosomiasis control: a review
10.16250/j.32.1374.2024138
- VernacularTitle:机器学习模型在血吸虫病防控中的应用
- Author:
Yu ZHOU
1
;
Yixin TONG
1
;
Yibiao ZHOU
1
Author Information
1. Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
- Publication Type:Journal Article
- Keywords:
Schistosomiasis;
Machine learning;
Artificial intelligence;
Application
- From:
Chinese Journal of Schistosomiasis Control
2024;36(5):535-541
- CountryChina
- Language:Chinese
-
Abstract:
Schistosomiasis is a major public health concern in the world, and precision control is crucial to combating this disease. Due to the complex and diverse transmission route of schistosomiasis, conventional statistical models have significant limitations for precision control of schistosomiasis. As an important branch of artificial intelligence, machine learning has shown remarkable advantages in schistosomiasis control and research. It has been shown that machine learning is highly effective for disease prediction and risk assessment, so as to optimize the disease control strategy and resource allocation and achieve the precision control target. This review summarizes the characteristics of machine learning models and their applications in the research of intermediate host snails and schistosomiasis.
