## 4 Pre-Processing and Classification
### Classification and Regression Models
[62] compared the performance of SVM, RF, and KNN that supports RF with the highest accuracy of 96%, specificity of 95%, sensitivity of 94%, and 0.93 \(F_{1}\) score. [PERSON] et al. [25] evaluated SVM, tree-based model, multinomial LR, KNN, Gaussian NB, XGBoost, and RF classifiers on EHR and environmental data. They followed an ensemble classifier method achieving 92.15% accuracy, 87.3% sensitivity, 94.8% specificity, 89.5% precision, and 0.878 AUC. Figure 7 illustrates the performance of various AI models in asthma management for improved decision-making. The given box plot compares the accuracy distributions of various ML and DL models. Each model is represented along the x-axis, while the y-axis shows accuracy values as percentages. The plot includes median values and interquartile ranges. Models like RF, XGBoost, and the CNN show higher accuracies, with fewer outliers, while Naive Bayes and KNN display a wider spread of results. The mean accuracy is highlighted by a black marker for each model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Advantage** & **Disadvantage** \\ \hline \multirow{2}{*}{Ensemble} & •Offers combining multiple models with high accuracy & •Elevated computational cost \\  & •Less overfitting chances compare to individual models & •Interpretation and results decision could be difficult \\ \hline \multirow{2}{*}{Hybrid} & •Benefit of various models strengths in single output prediction & •One weak model can lead to lower performance \\  & • Ability to handle complex patterns in the EHR data & •More training time and resources required \\ \hline \multirow{2}{*}{CNN} & •Efficient model for spatial data representation & •Training requires large datasets \\  & • Ability to learn complex patterns in the data automatically & •Higher computational complexity \\ \hline \multirow{2}{*}{Decision Tree} & •Tree structure makes results interpretation easy & •Likely to overfitting compare to other models \\  & •Provides quite fast training and inference & •Fewer effective for the complex relationship among data \\ \hline \multirow{2}{*}{ANN} & •Well handling of non-linear relationships among EHR data & •High chances of overfitting with small datasets \\  & • Ability to learn complex dependencies among data & •Dependent on excessive hyperparameter tuning \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Popular models in asthma classification with their benefits and drawbacks. Figure 7: Box plot showing the accuracy distribution of various machine learning and deep learning models including ensemble classifiers and hybrid models. Each box represents the spread of available accuracy data on y-axis for the respective model. [PERSON] et al. [63] considered EHRs that included personal characteristics, physiological data, and impulse oscillometry system profile and applied LR, RF, KNN, SVM, and multi-layer perceptron. The results revealed that the multi-layer perceptron achieved the highest accuracy of 78.60%. [PERSON] et al. [35] utilized respiratory sounds and environmental data in the evaluation of CNN, RNN, and STAIN (a hybrid form of the CNN and RNN) classifiers and reported that the STAIN acquired the highest accuracy of 93.4%, 92.7% sensitivity, and 94.2% specificity. [PERSON] et al. [43] utilized cough audio sounds in asthma prediction using the Gaussian mixture model by achieving an accuracy of 83.76%, 82.81% sensitivity, and 84.76% specificity. [PERSON] et al. [31] considered LR with transfer learning as a fully connected neural network and quantile regression method in asthma prediction on patients' demographics and environment data. The classification results showed performance with 73.8% accuracy, 72.7% sensitivity, 75.7% specificity, 68.70% precision, and 0.689 \(F_{1}\) score. [PERSON] et al. [64] conducted a review in analyzing temporal EHR data with the help of the RNN, CNN, LSTM, Autoencoder, GAN, and Transformer model. Likewise, [PERSON] et al. [65] reviewed the existing literature on the detection of chronic disease with a focus on several ML and DL models. [PERSON] et al.