A Prediction Model for Cardiovascular Health Risk from Air Quality Index of Pollution laden Environment

Abstract

The cardiovascular health concerns are triggered by various causative agents, of which environmental inhaled pollutants such as CO, NO, NO2, PM10, PM2.5, O3 and SO2 from is an important agents. The introduction of air pollutants is caused by human activities that introduce contaminants into the air. In Nigerian, people living in pollution laden environments are unknowingly exposed to these risks such as Asthma, cough, lung cancer etc. However, there is paucity of information on the health risk impacts on the people living in pollution laden environments. This is due to lack of predictive model to reveal the associated risk to enhance early detection and prevention. One of the methods to evaluate and predict the pollutant is the use of Air Quality Index (AQI) dataset. The quality of AQI data of an environment is a pointer to the degree of pollution and the health risk of the inhabitants. Existing predictive techniques such as Probability and Statistics model used to predict AQI were very complex with some level of uncertainty which necessitate an alternative approach for better accuracy. A Machine Learning (ML) approach combined with an associative decision rule was used to predict the air quality and to identify areas predominates with toxic air quality. Two datasets; open and locally sourced were used, data pre-processed and engineered implementation were done using python coding. The prediction models; Support Vector Classifier (SVC) and Random Forest Classifier (RFC) were employed. The performances of the models were evaluated using classification reports and confusion matrix metrics. The RFC gave an accuracy level of 99% and SVC an accuracy level of 83%. This results show that AQI predictions obtained through RFC is better in accuracy when compared with SVC.