Intelligent Inhalation Therapy for Cystic Fibrosis Using IoT and Machine Learning Solutions

Optimal patients result in the long-term management of cystic fibrosis (CF) and need personalized treatment strategies due to the hereditary disorder's impact on respiratory problems. One potential approach to individualized CF treatment is intelligent inhalation therapy systems that combine machine learning (ML) algorithms with Internet of Things (IoT) sensors. To provide CF patients with a predictive modeling framework that can personalize their inhalation treatment using the Random Forest algorithm. The algorithm is designed to predict individual-level outcomes, including the potential of an exacerbation and the efficacy of therapy, by mining a large and varied dataset that includes patient demographics, lung function measurements, medication adherence records, environmental factors, and reports of symptoms. The Random Forest method was selected due to its capacity to manage diverse data types, detect intricate relationships between variables, and provide insights that can be understood. It shows that the Random Forest model effectively predicts important outcomes for CF care by training, validating, and evaluating it using real-world data from CF patients. Exacerbation risks and patient-specific therapy responses may be better understood by feature significance analysis, allowing for more targeted intervention approaches. The proposed predictive modeling framework will improve the efficiency and effectiveness of inhalation treatment for managing CF. Healthcare practitioners may enhance the management of CF via ML analytics and data collected through the IoT. This allows for delivering personalized treatment regimens, optimizing resource allocation, and better patient outcomes. Challenges include ensuring data accuracy, managing extensive datasets, sustaining real-time monitoring, optimizing algorithms, and ensuring system stability.