Spatio-temporal modelling of asthma-prone areas using a machine learning optimized with metaheuristic algorithms
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi‐Niaraki, Soo-Mi Choi
Abstract
The main aim of this study was to use two metaheuristic optimization algorithms—a genetic algorithm (GA) and a teaching-learning-based optimization (TLBO) algorithm—to determine the optimal parameters of a support vector regression (SVR) model for Spatio-temporal modelling of asthma-prone areas in Tehran, Iran. First, a spatial-temporal database consisting of dependent (872 patients with asthma) and independent data (air pollution, meteorology, distance to park, and street parameters) was created. In the next step, Spatio-temporal modelling and mapping of asthma-prone areas were performed using three models: SVR, SVR-GA, and SVR-TLBO. The highest accuracy of the area under the curve (AUC) of the receiver operating characteristic (ROC) was for SVR-GA (0.806, 0.801, 0.823, and 0.811), SVR-TLBO (0.8, 0.797, 0.81, and 0.803), and SVR (0.786, 0.78, 0.796, and 0.791) models in spring, summer, autumn, and winter, respectively. Autumn, winter, spring, and summer were most accurate in modelling asthma occurrence, respectively.