Real-time Health Monitoring of Patients in Emergencies Through Machine Learning And IoT Integrated With Edge Computing

With employing the Internet of Things (IoT) sensors and modern-day machine learning algorithms, this study presents a modern inquiry at the intersection of healthcare and era. This have a look at studies the capability of Long ShortTerm Memory (LSTM) and Artificial Neural Network (ANN) fashions in forecasting affected person fitness consequences by utilising the exploitation of sensor facts inclusive of temperature, blood strain, ECG, EEG, and pulse price. The study extensively examines the models' performance utilising a broad variety of performance benchmarks. Results demonstrate that once compared to the LSTM model, the ANN version performs better in terms of prediction accuracy, precision, do not forget, and Fl score. This enhanced accuracy indicates how effectively the ANN model can spot challenging patterns in the dataset. The predicted fitness possibilities for 15 human individuals also are proved in a result table, reaffirming the ANN models constant advantage in prediction. This look at proactive patient care, more intelligent treatment regimens, and improved overall patient outcomes is driving the business and is a significant step towards personalised, data-driven healthcare solutions.