## 4 Pre-Processing and Classification
### Classification and Regression Models  
The primary goal of AI methods is to develop machine intelligence comparable to human cognition, achieved through advanced mathematical concepts and algorithms. As evident today, the advancements in these methods were not as pronounced in previous decades. Instead, these became highly efficient occasionally, with contributions from several scientists worldwide. Some models, such as the CNN, LSTM, and RNN perform well with the downside of high memory consumption, making functionality extremely hard with limited sources of central processing units. ML methods like SVM, RF, and NB can run on simple processors instead of graphics processing units and are efficient when handling specific data. The advantages and disadvantages of the models proven efficient in asthma classification (shown in Figure 7) are described in Table 3.  
ML and DL methods are commonly utilized in asthma analysis through predictions and classification. The choice and application of these methods also depend upon the nature of data, such as ML, which is chosen widely when considering EHR and environment-oriented textual data. Meanwhile, DL methods, i.e., the CNN and LSTM, are exercised when the data are more about imaging and sounds. The bagging technique refers to the average of the output of several classifiers with respect to stable results and was considered by [PERSON] et al. [45] using KNN, SVM, DT, RF, NB, and ANN methods that attained 98.84% accuracy. A review work by [PERSON] et al. [60] explored the importance of the SVM algorithm in CT chest scan classification. [PERSON] et al. [54] applied the XGBoost classifier on EHR and environmental data by obtaining 94.03% accuracy with 0.929 area under the curve (AUC) score. [PERSON] et al. [27] assessed LR, DT, SVM, and RF classifiers on allergens and EHR data, of which RF outperformed others with 87.8% accuracy, 87% sensitivity, 88% specificity, and 0.875 \(F_{1}\) score. [PERSON] et al. [44] adopted a new approach to asthma identification by analyzing volatile organic compounds (VOCs) from phenotype data in 79 exhaled breath samples using a portable gas chromatography device. Biomarker selection was performed using linear discriminant analysis, principal component analysis, and statistical methods. Results revealed that VOC profiling can be effective in diagnosing asthma and phenotyping. [PERSON]. [39] utilized EHR, bio-signals symptoms, and environmental data using XGBoost at two layers. First, the model predicted asthma attacks, followed by the recommended route, achieving 94% accuracy, with 95.2% recall on 5-fold cross-validation. The Mean Squared Error (MSE) quantifies the average squared differences between actual and predicted values, while the Root Mean Squared Error (RMSE), as the square root of the MSE, represents the average magnitude of prediction errors, and the Mean Absolute Error (MAE) calculates the average absolute differences. [PERSON] et al. [49] assessed the RF classifier on EHR data, reporting an RMSE of 2.48, MAE of 1.67, and MSE of 6.15, where the lowest error depicts the better performance. [PERSON] et al. [55] applied decision tree methods, including RF, random tree, the J48 model, and optimized forest on EHR data. The results revealed the least RMSE of 0.07 by the J48 model. [PERSON] et al. [56] applied LR, SVM, DT, KNN, and RF classifiers to EHR data, with RF achieving the highest performance at 98% accuracy, 98% precision, 95% recall, and an \(F_{1}\) score of 0.95. The air quality index classification problem was determined by [PERSON] et al. [48] on air quality data using SVM and RF models. In the study by [PERSON] et al. [61], a binary classification was applied based on the environmental data to classify safe and risky environments for asthma patients using SVM, KNN, and CNN models. The results showed that SVM attained 97.98% while RF showed 93.29% accuracy on two different location datasets. [PERSON] et al.