## 7 Addressing Research Questions  
This study examines various techniques designed to address key research questions related to asthma prediction and AI-based analysis. The following questions have been formulated to guide the investigation, focusing on the effectiveness, challenges, and advancements in ML, DL, and hybrid models. These questions aim to explore the role of diverse data sources, model performance, and future research directions in asthma assessment and management.  
* _How effectively can machine learning and deep learning models diagnose asthma compared to traditional diagnostic methods?_ The application of ML has demonstrated significant improvements in asthma prediction compared to both DL and traditional statistical methods. Notably, ensemble and hybrid ML models, along with DT, SVM, RF, and KNN, have shown predictive performance well when compared to DL approaches such as the RNN and CNN, as illustrated in Figure 7. Additionally, asthma analysis primarily relies on air quality and EHR data, as depicted in Figure 6b. Since these datasets are often presented in numerical form, ML techniques are particularly well suited for handling and analyzing them with greater precision.
* _How do different feature selection and data pre-processing techniques impact the performance of AI-based asthma prediction models?_ In numerical data analysis, pre-processing and feature selection techniques play a crucial role in enhancing asthma model performance, as discussed in Section 4.1 and illustrated in Figure 8. Eliminating null values and identifying outliers, which can introduce distortions in predictive analysis, are essential steps to prevent performance decline in AI models. Moreover, techniques such as correlation matrix analysis and principal component analysis help identify key features while reducing the influence of irrelevant variables. Additionally, data augmentation strategies like SMOTE contribute to improving model generalization by addressing class imbalance, ultimately leading to more reliable predictions.
* _How can multi-modal data integration (EHR, environmental, and physiological data) improve asthma forecasting and early intervention strategies?_ The review findings indicate that most studies have focused either on EHR and environmental data or separately on physiological signals. However, integrating these diverse data sources into a multi-modal framework can offer a more comprehensive understanding of asthma triggers. Combining environmental factors, patient medical history, and real-time physiological signals enhances feature representation, providing a more broad view of asthma progression. These enriched data merging can lead to improved predictive accuracy, enabling early intervention strategies and more personalized asthma management.
* _How can large language models (LLMs) like ChatGPT enhance asthma management, patient education, and decision support for physicians?_ With the growing prominence of LLMs in processing natural language and extracting meaningful insights, their integration into asthma care presents significant advantages. By transforming the outputs of traditional ML or DL models into conversational prompts, ChatGPT can provide personalized feedback, offering patients a clearer understanding of their health status, as illustrated in Figure 8. Additionally, preliminary studies, discussed in Section 5, highlight the potential of ChatGPT in assisting physicians with decision support and improving patient education, hence enhancing asthma management strategies.