## 4 Pre-Processing and Classification
### Classification and Regression Models
[26] evaluated DNN and KNN performance on EHR data, which found KNN to be the best model with 95% accuracy, 100% sensitivity, and 92.7% specificity. [PERSON] et al. [20] assessed convolutional LSTM and a hybrid form of CNN-LSTM on environmental data. The results demonstrated that their proposed hybrid model obtained the lowest MAE of 4.16 and R squared of 0.94. [PERSON] et al. [30] compared the performance of the ANN, DNN, fully connected neural network, and CNN on EHR and environmental data. The outcomes revealed that the CNN outperformed others with the least MAE of 2.12 on overall data, 1.09 on individual patients, RMSE of 2.42 on overall data, and 1.36 on individual patients. [PERSON] et al. [32] examined the performance of LSTM, XGBoost, gradient boosting regressor, SVM, and extra trees regressor on environmental data and reported LSTM with the lowest MSE and MAE. In the review article by [PERSON] et al. [66], the authors expressed that the CNN is the most common DL model while SVM is the most common ML model. The spirometry EHR data were classified by [PERSON] et al. [33] using SVM, RF, NB, and MLP models, which indicated that MLP outperformed others with 85.7% test accuracy on five-fold cross-validation. [PERSON] et al. [36] examined the RNN, FastAI tabular model with Prophet time series algorithm, and multioutput regressor for predictions on environmental data. Their proposed model, DL with categorical embedding, achieved the lowest an RMSE of 8.31 in \(NO_{2}\) predictions, 5.34 in \(PM_{2.5}\), and 5.44 in \(PM_{10}\) prediction. [PERSON] et al. [67] evaluated LSTM, GRU, RNN, CNN, sample convolution and interaction network (SCINet), and a proposed hybrid model, SCINet-CRINet. The results showed that SCINet-CRINet achieved the lowest error, with RMSE of 28.41 and MAE of 24.94 on the Beijing \(PM_{2.5}\) dataset for \(PM_{2.5}\) pollution prediction. [PERSON] et al. [58] applied a temporal convolutional network with multi-head self-attention and a combination of light gradient boosting machines on the environmental dataset. Study results revealed that their proposed model obtained the lowest RMSE of 60.2 and 4.6 MAE. Table 4 provides a comparative analysis of AI models used for asthma and lung disease prediction, highlighting their performance based on dataset type and pre-processing techniques. Hybrid models combining CNN, RNN, and traditional classifiers generally achieved higher accuracy. For example, the ResNet-18 and SVM hybrid model reached 99.1%, while the bagging ensemble (KNN, SVM, DT, RF, NB, ANN) performed similarly at 98.84%. The CNN-RNN hybrid model also demonstrated strong predictive performance with an accuracy of 93.4%. Traditional ML models, such as decision trees (RMSE: 0.07) and SVM (absolute error reduction of 15%), exhibited competitive results. XGBoost models  
maintained stable accuracy of about 94%, whereas the Gaussian mixture model had the lowest accuracy among the listed approaches (83.76%). Table 4 also highlights performance variations based on data types and pre-processing techniques. Models trained on both environmental and EHR data generally achieved higher accuracy, particularly when combined with data augmentation, i.e., SMOTE or advanced data handling approaches including outlier removal, feature selections, and missing values handling. For instance, the XGBoost model utilizing both data augmentation and data handling reached 94% accuracy, while the hybrid TCN-gradient boosting model, trained entirely on environmental data, showed elevated error values (RMSE: 60.2, MAE: 4.36). Models incorporating comprehensive pre-processing steps, such as data handling and augmentation, tended to outperform those relying on limited or no pre-processing. Overall, DL and hybrid approaches demonstrate superior predictive performance, with ensemble models and boosting techniques maintaining strong accuracy across different datasets.