Web application development using multiple regression analysis to predict silicosis risk among stone carvers in Nakhon Ratchasima, Thailand

To develop a web-based application that uses multiple regression analysis to predict the risk of silicosis among stone carvers in Nakhon Ratchasima, Thailand. Data from 243 stone carvers were used to construct a multiple regression model incorporating key associated variables key associated variables: concentration of respirable silica dust, daily working hours, presence of underlying diseases, and residential proximity to the workplace. Model performance was assessed via R², adjusted R², RMSE, and the significance of associated variables. The model was integrated into a user-friendly web application and deployed for real-time risk assessment among 362 stone carvers. Silicosis risk scores were categorized into five levels to facilitate interpretation and targeted interventions. The Mann‒Whitney U test was applied to compare silicosis risk scores before and after application. The regression model explained 66.2% of the variance in silicosis risk scores (adjusted R² = 0.662), with strong predictive accuracy (RMSE = 2.59). All predictor variables were statistically significant (p < 0.05). The web application assigned silicosis risk scores ranging from 12 to 25, with 77.1% of participants classified as “very high risk.” However, no statistically significant difference was observed between the model and web application silicosis risk scores (p = 0.155); nonetheless, the observed trend suggests potential benefits in enhancing worker awareness and promoting protective behaviors. The developed multiple regression model and web application provide an effective tool for real-time silicosis risk prediction and stratification in stone carving communities. This digital health tool shows promise for early risk detection and prevention of silicosis in workers.