## 6 Challenges and Opportunities  
Pre-processing techniques and various AI models were emphasized for their role in achieving advanced performance in predicting asthma triggers and environmental predictions. However, some gaps in the research continue to exist and should be addressed. The current review highlighted these shortcomings and suggested new ways to conduct novel research within the presented area.  
* Classifying cough sounds into binary classes, as performed in the study by [35] using time-frequency analysis, can be extended to recommend relatively safer travel routes. Similarly, the binary classification of asthma vs. normal samples [43] uses relatively small datasets, which may impact model generalization. This review suggests that leveraging a high-volume dataset, such as the Kaggle 2018 free sound dataset with around 18,000 samples across 41 categories (including 'Cough'), could provide significant benefits. It could be useful for pre-training and transfer learning, for example, by training a base model on the entire dataset and then fine-tuning it with 'cough' samples. Additionally, analyzing samples using efficient pre-processing techniques like Fourier or wavelet transforms could further enhance model performance.
* Since ML and DL algorithms are often viewed as black-box models for classification and regression tasks, analyzing the results can be challenging, making it difficult to identify strong and weak performance. Explainable AI techniques like 'Shapley Additive Explanations' can improve understanding of the decision-making processes of ML and DL models in asthma analysis, especially for regression and forecasting tasks. These methods can enhance model reliability by revealing how clinical and environmental factors influence predictions, helping to better predict asthma outcomes.
* AI models typically focus on maximizing intra-class similarity and minimizing inter-class similarity to improve performance. However, individual-based asthma classification presents a challenge, as it involves variation in classification labels, such as healthy vs. sick, or categorizing into the three asthma zones--green, yellow, and red for each individual dataset--requiring a more efficient approach to deal with the issue. This review suggests a step-by-step methodology to improve model generalization and performance: first, develop a generalized model that predicts asthma zones  
Figure 8: A proposed process flow for asthma analysis illustrating commonly used data collection methods, pre-processing techniques with AI algorithms, and potential applications. Conversational prompts, derived from clean data and model outputs, provides natural language-based personalized feedback through ChatGPT.  
based on population-level data rather than individual cases. Then, perform reliable zone transitions to help enhance outcomes when addressing individual-based asthma conditions. This approach could lead to more accurate and consistent predictions for individuals.
* Asthma classification using respiratory sounds collected from stethoscopes or smartphone devices is a relatively new area, in contrast to the more established use of EHR data. Weather and environmental factors also play a crucial role in predicting asthma triggers. Therefore, combining respiratory sounds with weather and environmental data could enhance prediction accuracy, mainly through bagging and ensemble classifier approaches. Interpreting the results by ChatGPT can further improve patient feedback.
* The review highlighted the use of standalone ML, DL, and hybrid models to achieve satisfactory performance. However, transformer models, particularly in NLP, such as ChatGPT-based models, are gaining popularity. Therefore, evaluating GPT prompts with linguistic forms of EHR, pollution, and weather data for LLMs is worth considering.