Integrating Wearable Sensor Data and AI for Remote Monitoring and Management of Chronic Respiratory Diseases

Chronic respiratory illnesses exert a major strain on healthcare systems globally, necessitating creative ways to optimize patient outcomes and treatment. This work presents a new technique for remote monitoring and treatment of chronic respiratory illnesses that blends wearable sensor data with artificial intelligence (AI). The method's properties are highlighted, including the employment of Random Forest, Long Short-Term Memory (LSTM), and Support Vector Machines (SVM) as three important algorithms for modeling illness development, forecasting exacerbations, and spotting abnormalities in real-time patient data. Our method has proven to be a big success. It is now conceivable to consistently forecast illness exacerbations, which speeds up medical treatments and decreases the likelihood of life-threatening respiratory crises. Real-time vital sign monitoring gives comprehensive, up-to-date data to patients and healthcare practitioners while allowing people to actively govern their own health. Real-time feedback and individualized treatment regimens assist in enhancing patient involvement. By addressing privacy and ethical concerns, adequate data management has been achieved. The integration of wearable sensors with AI provides a considerable improvement in the treatment of chronic respiratory illnesses. It enables for a more proactive, patient-centered, and data-driven approach to healthcare. The results not only boost patient outcomes, but also offer the ability to minimize healthcare expenses and deliver tailored therapy on a worldwide scale.