A Web-Based Supervised Machine Learning Model for Air Quality Index and Respiratory Care Prediction

The quality of the air is crucial for people who suffer from respiratory conditions. The prediction of the air quality index (AQI) is essential for preventing health issues, particularly for those with respiratory diseases. Deteriorating air quality is often linked to increased residential, industrial, and transportation activity. Accurate AQI prediction can help in mitigating these health risks. Disseminating information about the air quality index among diverse populations is crucial. The motive of this study is to use several machine learning regression and classification approaches to develop quantitative and accurate air quality forecast models. Incorporating a preventive system that provides preventive measures to healthy people as well as those suffering from cancer and respiratory diseases is an additional component of this. This study evaluates multiple supervised machine learning algorithms, including Random Forest and XGBoost, for AQI prediction using real-time data from four Indian cities. To identify the most effective model, five different machine learning models were used, and hyperparameter tuning was performed to achieve better accuracy. After evaluating the R2 score, root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) for five different regression algorithms, as well as precision, accuracy, recall, and F1 score for classification algorithms, it was found that the Random Forest and XGBoost (using the best hyperparameters) classifiers performed best among other models. The Random Forest model achieved an MAE of 0.97 and an R-squared of 0.998, while the XGBoost classifier reached a perfect accuracy of 1.0 after hyperparameter tuning, providing valuable insights for respiratory care and preventive measures. Other models that were looked at included k-nearest neighbors (KNN), decision trees, and support vector machines (SVM). When visiting locations with high air quality index (AQI) values, exercise carefulness since these circumstances might lead to various health concerns.