IoT-Enabled Health Monitoring System for Cardiovascular Disease Prediction Using Deep Learning Models

Internet of Things technology is progressing rather quickly, and this primary application affects healthcare mainly in prevention of diseases and constantly monitoring one’s health. Internet of Things involve creation of various devices with sensors and allow the collection of patient data round the clock making it easier to diagnose preliminary health oddities. It also increases disease control through real-time data to the healthcare givers thus improving the patient’s result. In this research, we employ a dataset containing records from IoT sensors with patients’ data, the cases here are detection of heart disease and other chronic diseases. Hear study proposes the use of Convolutional Neural Network CNN and Long Short-Term Memory LSTM NNs for predictive analysis on the IoT data received. These models are specifically designed to recognize sign of possible health complications and placed much emphasis on the use of diagnostics with reduced false positive results. This way, using data augmentation and adjusting the model parameters, I was able to raise the predictive accuracy to 95% and above. This actually shows how the proposed system is advantageous for IoT in the field of health since it is capable of lessening response time and global correct evaluation of health. It may find application in health systems in improving disease prevention across populations and most importantly in developing healthcare systems.