Spatial Predictive Modeling of Liver Fluke <i>Opisthorchis viverrine</i> (<i>OV</i>) Infection under the Mathematical Models in Hexagonal Symmetrical Shapes Using Machine Learning-Based Forest Classification Regression
Benjamabhorn Pumhirunroj,
Patiwat Littidej,
Thidarut Boonmars,
Atchara Artchayasawat,
Narueset Prasertsri,
Phusit Khamphilung,
Satith Sangpradid,
Nutchanat Buasri,
Theeraya Uttha,
Donald Slack
Affiliations
Benjamabhorn Pumhirunroj
Program in Animal Science, Faculty of Agricultural Technology, Sakon Nakhon Rajabhat University, Sakon Nakhon 47000, Thailand
Patiwat Littidej
Geoinformatics Research Unit for Spatial Management, Department of Geoinformatics, Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, Thailand
Thidarut Boonmars
Department of Parasitology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
Atchara Artchayasawat
Department of Agriculture and Resources, Faculty of Natural Resources and Agro-Industry, Kasetsart University, Chalermphrakiat Sakon Nakhon Province Campus, Sakon Nakhon 47000, Thailand
Narueset Prasertsri
Geoinformatics Research Unit for Spatial Management, Department of Geoinformatics, Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, Thailand
Phusit Khamphilung
Geoinformatics Research Unit for Spatial Management, Department of Geoinformatics, Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, Thailand
Satith Sangpradid
Geoinformatics Research Unit for Spatial Management, Department of Geoinformatics, Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, Thailand
Nutchanat Buasri
Geoinformatics Research Unit for Spatial Management, Department of Geoinformatics, Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, Thailand
Theeraya Uttha
Geoinformatics Research Unit for Spatial Management, Department of Geoinformatics, Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, Thailand
Donald Slack
Department of Civil & Architectural Engineering & Mechanics, University of Arizona, 1209 E. Second St., P.O. Box 210072, Tucson, AZ 85721, USA
Infection with liver flukes (Opisthorchis viverrini) is partly due to their ability to thrive in habitats in sub-basin areas, causing the intermediate host to remain in the watershed system throughout the year. Spatial modeling is used to predict water source infections, which involves designing appropriate area units with hexagonal grids. This allows for the creation of a set of independent variables, which are then covered using machine learning techniques such as forest-based classification regression methods. The independent variable set was obtained from the local public health agency and used to establish a relationship with a mathematical model. The ordinary least (OLS) model approach was used to screen the variables, and the most consistent set was selected to create a new set of variables using the principal of component analysis (PCA) method. The results showed that the forest classification and regression (FCR) model was able to accurately predict the infection rates, with the PCA factor yielding a reliability value of 0.915. This was followed by values of 0.794, 0.741, and 0.632, respectively. This article provides detailed information on the factors related to water body infection, including the length and density of water flow lines in hexagonal form, and traces the depth of each process.
