## 6 Discussion  
This paper explores the integration of machine learning (ML) techniques in the field of sleep medicine, specifically focusing on the diagnosis and management of Obstructive Sleep Apnea (OSA). OSA's complexity, variability, and the limitations of traditional diagnostic methods like PSG underscore the need for more efficient, precise, and accessible tools. The proposed digital platform leverages ML models to assist healthcare professionals by streamlining the diagnostic process and supporting the development of personalized treatment plans tailored to each patient's unique needs. By analyzing data that include genetic, lifestyle, and environmental factors, these models can recommend targeted therapies, improving treatment outcomes and reducing adverse effects. A significant challenge in developing such systems is the quality and labeling of data. The dataset provided included many epochs without labeled events, necessitating the assignment of default labels such as \"no apnea event.\" However, this approach introduces uncertainty, as undetected or mislabeled apnea events may affect the model's training. For instance, the labeling of adjacent epochs where apnea events were not confirmed by specialists may have led to misclassifications that impact the model's learning ability. To improve the model's accuracy, more robust validation processes for event labels, possibly involving domain experts, are essential. This would provide more reliable training data and improve model detections, contributing to the effectiveness of digital platforms in real-world clinical settings. Figure 10: SpO2 of patient Z with confusion matrix labels. Feature engineering also played a critical role in enhancing the model's performance. By incorporating features such as variance based on past data, the model gained better context for interpreting SpO2 and pulse rate fluctuations, which proved pivotal in detecting apnea events. Features like standard deviation from previous sequences helped improve recall by giving the model more context about the fluctuations in the signal, ultimately leading to better detections. The importance of feature selection and engineering will continue to evolve as more sophisticated data become available, including polysomnography results, genetic data, and real-time inputs from wearable devices. Despite promising results, the current model still faces challenges, such as reducing false positives and minimizing missed apnea events (false negatives). The model's tendency to predict apnea events during periods of high variability in the SpO2 signal indicates the need for refined feature engineering and the exploration of more advanced model architectures. Moreover, optimizing hyperparameters and incorporating additional features such as body position could improve the model's detective accuracy. In summary, the integration of machine learning into sleep medicine presents a promising advancement in the diagnosis and management of OSA. While the current model shows potential, further efforts will be focused on improving data quality, refining feature engineering, and enhancing model accuracy through collaboration with medical professionals. The future of sleep medicine relies on the seamless integration of these technologies to provide personalized, efficient, and accurate treatments, ultimately leading to better health outcomes for patients with OSA. The continuous refinement and validation of machine learning models, combined with the development of more sophisticated algorithms, will further enhance diagnostic accuracy and support personalized treatment plans in clinical settings. To address the model's tendency to overpredict apnea events during periods of high SpO2 variability, future work will explore additional feature engineering techniques, such as incorporating body position and sleep stage as input features.