Air pollution is a significant health concern worldwide, contributing to
various respiratory diseases. Advances in air quality mapping, driven by the
emergence of smart cities and the proliferation of Internet-of-Things sensor
devices, have led to an increase in available data, fueling momentum in air
pollution forecasting. The objective of this study is to devise an integrated
approach for predicting air quality using image data and subsequently assessing
lung disease severity based on Air Quality Index (AQI).The aim is to implement
an integrated approach by refining existing techniques to improve accuracy in
predicting AQI and lung disease severity. The study aims to forecast additional
atmospheric pollutants like AQI, PM10, O3, CO, SO2, NO2 in addition to PM2.5
levels. Additionally, the study aims to compare the proposed approach with
existing methods to show its effectiveness. The approach used in this paper
uses VGG16 model for feature extraction in images and neural network for
predicting AQI.In predicting lung disease severity, Support Vector Classifier
(SVC) and K-Nearest Neighbors (KNN) algorithms are utilized. The neural network
model for predicting AQI achieved training accuracy of 88.54 % and testing
accuracy of 87.44%,which was measured using loss function, while the KNN model
used for predicting lung disease severity achieved training accuracy of 98.4%
and testing accuracy of 97.5% In conclusion, the integrated approach presented
in this study forecasts air quality and evaluates lung disease severity,
achieving high testing accuracies of 87.44% for AQI and 97.5% for lung disease
severity using neural network, KNN, and SVC models. The future scope involves
implementing transfer learning and advanced deep learning modules to enhance
prediction capabilities. While the current study focuses on India, the
objective is to expand its scope to encompass global coverage.

通过使用图像数据预测空气质量、评估肺病严重程度的整合方法，以提高 AQI 和肺病严重程度的准确性，并达到了 87.44% 的 AQI 预测准确率和 97.5% 的肺病严重程度评估准确率。