Unsupervised machine learning in air pollution epidemiology in South Africa

This dataset consist of different scripts and do files, used to achieve objectives to assess the applicability of machine learning in air pollution epidemiology in South Africa. The STATA do files were used to investigate the artificial intelligence (AI) survey distributed among postgraduate diploma students at the School of Health Systems and Public Health. R scripts were used for data imputation i.e., kalman, mice and mtsdi imputation, for the missing air pollution data and meteorological conditions. R scripts were also used for classification and regression trees to investigate joint effects of PM10, PM2.5, NO2, SO2 and O3 on respiratory and cardiovascular hospital admissions. Again presented are the R scripts for the unsupervised machine learning clustering methods i.e., k-means clustering, spectral clustering, dbscan clustering for joint effects for PM10, PM2.5, NO2, SO2 and O3 on respiratory and cardiovascular hospital admissions.