{"title":"[基于5种机器学习模型的安徽省钉螺传播区域预测]。","authors":"F Gao","doi":"10.16250/j.32.1915.2024085","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To predict the areas of <i>Oncomelania hupensis</i> snail spread in Anhui Province from 1977 to 2023 using machine learning models, and to compare the effectiveness of different machine learning models for prediction of areas of <i>O. hupensis</i> snail spread, so as to provide insights into investigating the trends in areas of <i>O. hupensis</i> snail spread.</p><p><strong>Methods: </strong>Data pertaining to <i>O. hupensis</i> snail spread in Anhui Province from 1977 to 2023 were collected and a database was created. Five machine learning models were created using the software Matlab R2019b, including support vector regression (SVR), nonlinear autoregressive (NAR) neural network, back propagation (BP) neural network, gated recurrent unit (GRU) neural network and long short-term memory (LSTM) neural network models, and the model fitting effect was evaluated with mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (<i>R</i><sup>2</sup>). Following model training, the areas of <i>O. hupensis</i> snail spread were predicted in Anhui Province from 2024 to 2030.</p><p><strong>Results: </strong>The cumulative areas of <i>O. hupensis</i> snail spread were 40 241.32 hm<sup>2</sup> in Anhui Province from 1977 to 2023, and the area of <i>O. hupensis</i> snail spread varied greatly among years, with a periodic peak every 4 to 6 years. The fitting curves of SVR, NAR neural network, BP neural network, GRU neural network and LSTM neural network models were increasingly closer to the real curves for areas of <i>O. hupensis</i> snail spread in Anhui Province. The trends in areas of <i>O. hupensis</i> snail spread in Anhui Province from 2024 to 2030 appeared approximately \"M\"-shaped curves by SVR and NAR neural network models, approximately \"W\"-shaped curves by BP and GRU neural network models, and a unimodal conical curve by the LSTM neural network model. The LSTM neural network model had the best effect for predicting areas of <i>O. hupensis</i> snail spread in Anhui Province, with the RMSE of 1 277 480, MAE of 797 422 and <i>R</i><sup>2</sup> of 0.978 9, respectively.</p><p><strong>Conclusions: </strong>Among the five models, The LSTM neural network model has a high efficiency for predicting areas of <i>O. hupensis</i> snail spread in Anhui Province, which may serve as a tool to investigate the trends in areas of <i>O. hupensis</i> snail spread.</p>","PeriodicalId":38874,"journal":{"name":"中国血吸虫病防治杂志","volume":"36 6","pages":"572-576"},"PeriodicalIF":0.0000,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[Prediction of areas of <i>Oncomelania hupensis</i> snail spread in Anhui Province based on five machine learning models].\",\"authors\":\"F Gao\",\"doi\":\"10.16250/j.32.1915.2024085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To predict the areas of <i>Oncomelania hupensis</i> snail spread in Anhui Province from 1977 to 2023 using machine learning models, and to compare the effectiveness of different machine learning models for prediction of areas of <i>O. hupensis</i> snail spread, so as to provide insights into investigating the trends in areas of <i>O. hupensis</i> snail spread.</p><p><strong>Methods: </strong>Data pertaining to <i>O. hupensis</i> snail spread in Anhui Province from 1977 to 2023 were collected and a database was created. Five machine learning models were created using the software Matlab R2019b, including support vector regression (SVR), nonlinear autoregressive (NAR) neural network, back propagation (BP) neural network, gated recurrent unit (GRU) neural network and long short-term memory (LSTM) neural network models, and the model fitting effect was evaluated with mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (<i>R</i><sup>2</sup>). Following model training, the areas of <i>O. hupensis</i> snail spread were predicted in Anhui Province from 2024 to 2030.</p><p><strong>Results: </strong>The cumulative areas of <i>O. hupensis</i> snail spread were 40 241.32 hm<sup>2</sup> in Anhui Province from 1977 to 2023, and the area of <i>O. hupensis</i> snail spread varied greatly among years, with a periodic peak every 4 to 6 years. The fitting curves of SVR, NAR neural network, BP neural network, GRU neural network and LSTM neural network models were increasingly closer to the real curves for areas of <i>O. hupensis</i> snail spread in Anhui Province. The trends in areas of <i>O. hupensis</i> snail spread in Anhui Province from 2024 to 2030 appeared approximately \\\"M\\\"-shaped curves by SVR and NAR neural network models, approximately \\\"W\\\"-shaped curves by BP and GRU neural network models, and a unimodal conical curve by the LSTM neural network model. The LSTM neural network model had the best effect for predicting areas of <i>O. hupensis</i> snail spread in Anhui Province, with the RMSE of 1 277 480, MAE of 797 422 and <i>R</i><sup>2</sup> of 0.978 9, respectively.</p><p><strong>Conclusions: </strong>Among the five models, The LSTM neural network model has a high efficiency for predicting areas of <i>O. hupensis</i> snail spread in Anhui Province, which may serve as a tool to investigate the trends in areas of <i>O. hupensis</i> snail spread.</p>\",\"PeriodicalId\":38874,\"journal\":{\"name\":\"中国血吸虫病防治杂志\",\"volume\":\"36 6\",\"pages\":\"572-576\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中国血吸虫病防治杂志\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.16250/j.32.1915.2024085\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国血吸虫病防治杂志","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.16250/j.32.1915.2024085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
[Prediction of areas of Oncomelania hupensis snail spread in Anhui Province based on five machine learning models].
Objective: To predict the areas of Oncomelania hupensis snail spread in Anhui Province from 1977 to 2023 using machine learning models, and to compare the effectiveness of different machine learning models for prediction of areas of O. hupensis snail spread, so as to provide insights into investigating the trends in areas of O. hupensis snail spread.
Methods: Data pertaining to O. hupensis snail spread in Anhui Province from 1977 to 2023 were collected and a database was created. Five machine learning models were created using the software Matlab R2019b, including support vector regression (SVR), nonlinear autoregressive (NAR) neural network, back propagation (BP) neural network, gated recurrent unit (GRU) neural network and long short-term memory (LSTM) neural network models, and the model fitting effect was evaluated with mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2). Following model training, the areas of O. hupensis snail spread were predicted in Anhui Province from 2024 to 2030.
Results: The cumulative areas of O. hupensis snail spread were 40 241.32 hm2 in Anhui Province from 1977 to 2023, and the area of O. hupensis snail spread varied greatly among years, with a periodic peak every 4 to 6 years. The fitting curves of SVR, NAR neural network, BP neural network, GRU neural network and LSTM neural network models were increasingly closer to the real curves for areas of O. hupensis snail spread in Anhui Province. The trends in areas of O. hupensis snail spread in Anhui Province from 2024 to 2030 appeared approximately "M"-shaped curves by SVR and NAR neural network models, approximately "W"-shaped curves by BP and GRU neural network models, and a unimodal conical curve by the LSTM neural network model. The LSTM neural network model had the best effect for predicting areas of O. hupensis snail spread in Anhui Province, with the RMSE of 1 277 480, MAE of 797 422 and R2 of 0.978 9, respectively.
Conclusions: Among the five models, The LSTM neural network model has a high efficiency for predicting areas of O. hupensis snail spread in Anhui Province, which may serve as a tool to investigate the trends in areas of O. hupensis snail spread.
期刊介绍：
Chinese Journal of Schistosomiasis Control (ISSN: 1005-6661, CN: 32-1374/R), founded in 1989, is a technical and scientific journal under the supervision of Jiangsu Provincial Health Commission and organised by Jiangsu Institute of Schistosomiasis Control. It is a scientific and technical journal under the supervision of Jiangsu Provincial Health Commission and sponsored by Jiangsu Institute of Schistosomiasis Prevention and Control. The journal carries out the policy of prevention-oriented, control-oriented, nationwide and grassroots, adheres to the tenet of scientific research service for the prevention and treatment of schistosomiasis and other parasitic diseases, and mainly publishes academic papers reflecting the latest achievements and dynamics of prevention and treatment of schistosomiasis and other parasitic diseases, scientific research and management, etc. The main columns are Guest Contributions, Experts‘ Commentary, Experts’ Perspectives, Experts' Forums, Theses, Prevention and Treatment Research, Experimental Research, The main columns include Guest Contributions, Expert Commentaries, Expert Perspectives, Expert Forums, Treatises, Prevention and Control Studies, Experimental Studies, Clinical Studies, Prevention and Control Experiences, Prevention and Control Management, Reviews, Case Reports, and Information, etc. The journal is a useful reference material for the professional and technical personnel of schistosomiasis and parasitic disease prevention and control research, management workers, and teachers and students of medical schools.
The journal is now included in important domestic databases, such as Chinese Core List (8th edition), China Science Citation Database (Core Edition), China Science and Technology Core Journals (Statistical Source Journals), and is also included in MEDLINE/PubMed, Scopus, EBSCO, Chemical Abstract, Embase, Zoological Record, JSTChina, Ulrichsweb, Western Pacific Region Index Medicus, CABI and other international authoritative databases.
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