Deep Learning Analysis of Respiratory Health in India: The Role of Air Quality

In this study, we explore how machine learning can help track air pollution more effectively. Our research finds that the LSTM (Long Short-Term Memory) model performs exceptionally well in predicting pollution levels compared to earlier approaches. Interestingly, when dealing with time-series data, CNNs (Convolutional Neural Networks) have proven to be even more effective than traditional RNNs (Recurrent Neural Networks). We've also observed that as the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values decrease, the accuracy of pollution detection improves-giving us a more reliable way to anticipate and respond to air quality issues. Beyond the technical side, air pollution has very real consequences for public health. It's been linked to rising cases of respiratory illnesses, including acute infections, influenza, and pneumonia-conditions that frequently land people in emergency rooms. Data from AQI India highlights that prolonged exposure to pollutants like carbon monoxide (CO), fine particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2) has contributed to increasing mortality rates.