Spatio-temporal modelling of asthma-prone areas using a machine learning optimized with metaheuristic algorithms

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.

Keywords:

• Asthma
• spatio-temporal modelling
• machine learning model
• metaheuristic algorithms
• geographic information system (GIS)

Acknowledgment

This research was supported by the Ministry of Trade, Industry and Energy (MOT IE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program (Project No. P0016038).

Disclosure statement

No potential conflict of interest was reported by the authors.