IoT enabled health monitoring system using rider optimization algorithm and joint process estimation

The timely detection of abnormal health conditions is crucial in achieving successful medical intervention and enhancing patient outcomes. Despite advances in health monitoring, existing methods often struggle with achieving high accuracy, sensitivity, and specificity in real-time detection. This work addresses the need for improved performance in health monitoring systems in real time sensor data. In this work, real-time health monitoring data is obtained through the utilization of MAX 30102 and LM35 sensors, which capture the physiological features such as heart rate, blood oxygen levels and body temperature. The acquired data from these sensors is then transmitted to ThingSpeak, a cloud-based platform developed for the Internet of Things (IoT), where the data are analysed. In order to ensure consistency, the sensed features are subjected to a standardization process, ensuring they are scaled uniformly. In this work joint process estimator rider optimization algorithm (JPEROA) for Deep stack auto-encoder is proposed to perform the classification task. In JPEROA algorithm line coefficients and delay coefficients parameters are estimated to improve the performance of the system. The performance of the proposed method is compared with other five machine learning algorithms, including Support Vector Machine, Random Forest, Gradient Boosting, Naive Bayes, and Multilayer Perceptron neural networks. The proposed method also evaluated using PTB Diagnostic dataset signals. The performance of the algorithms is assessed using multiple performance metrics such as accuracy, sensitivity and specificity. The proposed method provides a maximum accuracy of 0.9625 and maximum sensitivity of 0.975 and specificity of 0.95.