Wearable Sepsis Early Warning Using Cloud Computing and Logistic Regression Predictive Analytics

The significant morbidity and death rates associated with sepsis indicate that it is still an important health care concern. Wearable technologies, cloud computing, and logistic regression predictive analytics are proposed as unique techniques to identify sepsis early on in this research. Wearable sensors continuously monitor physiological parameters, collecting real-time data and sending it to the cloud for analysis. A logistic regression model trained using prior patient data can analyze the incoming data and predict the chance of sepsis development. The cloud's scalability, adaptability, and real-time processing communicate the necessity for quick interventions. The predictive analytics method shows some encouraging accuracy in spotting the first symptoms of sepsis and alerting doctors just in time. Combining wearable technology with cloud computing enhances accessibility to crucial health data, permitting remote monitoring and proactive healthcare management. The feasibility of the suggested approach in early sepsis diagnosis has been shown via both simulated and real-world patient studies. This system could enhance patient outcomes by facilitating early interventions and individualized healthcare recommendations. The cloud-based solution's scalability and flexibility also open the door to more widespread use in predictive analytics for a wide range of medical disorders.