Machine Learning for Epidemiological Analysis in The Industrial Area for a Sustainable Life

Abstract

Pollution exposure and human health in the industry contaminated area are always a concern. The need for industrialization urges to concentrate on sustainable life of residents in the vicinity of the industrial area rather than opposing the industrialists. Literature in epidemiological studies reveal that air pollution is one of the major problems for health risks faced by residents in the industrial area. Main pollutants in industry related air pollution are particulate matter (PM2.5, PM10), SO2 , NO2 , and other pollutants upon the industry. Data for epidemiological studies obtained from different sources which are limited to public access include residents’ sociodemographic characters, health problems, and air quality index for personal exposure to pollutants. This combined data and limited resources make the analysis more complex so that statistical methods cannot compensate. Our review finds that there is an increase in literature that evaluates the connection between ambient air pollution exposure and associated health events of residents in the industrially polluted area using statistical methods, mainly regression models. A very few applies machine learning techniques to figure out the impact of common air pollution exposure on human health. Most of the machine learning approach to epidemiological studies end up in air pollution exposure monitoring, not to correlate its association with diseases. A machine learning approach to epidemiological studies can automatically characterize the residents’ exposure to pollutants and its associated health effects. Uniqueness of the model depends on the appropriate exhaustive data that characterizes the features, and machine learning algorithm used to build the model. In this contribution, we discuss various existing approaches that evaluate residents’ health effects and the source of irritation in association with air pollution exposure, focuses machine learning techniques and mathematical background for epidemiological studies for residents’ sustainable life.