Sensor Data Fusion in Healthcare Monitoring System with Appropriate Rule-based Model for Error Reduction

Healthcare monitoring System (HMS) is involved in the continuous and periodic evaluation of the patient or individuals. The HMS uses multiple sensors to monitor the health status of patients. However, the conventional methods are subjected to errors in the computation of data in the environment. Hence, this paper proposed an Optimized Rule-based Sugeno Fuzzy Hidden Markov Model Stacked Deep Learning (ORSF-HMM-SDL) for error reduction in the environment. The proposed ORSF-HMM-SDL model uses the Associative rule-based for the computation of the health status of patients. The proposed model utilizes the optimized fuzzy system for the estimation of the features with the classification using stacked deep learning. The ORSF-HMM-SDL uses the Sugeno Fuzzy interface model to assess the health status of patients. The proposed ORSF-HMM-SDL estimates the Hidden Markov Model (HMM) for the computation of features for the error computation. With the estimated features the ORSF-HMM-SDL model computes deep learning for the classification with the stacked model. data fused with the sensor are applied and classified with the deep learning model. The simulation results demonstrated the effectiveness of different fusion techniques—ORSF-HMM-SDL Fusion, Kalman Filter Fusion, Deep Learning (DL) Based Fusion, and SVM Based Fusion—in healthcare monitoring systems. The study evaluates their performance using metrics such as accuracy, sensitivity, specificity, precision, recall, F1-score, error rate, latency, and throughput. The Deep Learning Fusion method achieves the highest accuracy of 96.5% for heart disease detection, 94.2% for diabetes, and 95.8% for hypertension, with an overall accuracy of 98.3% for healthy individuals. The method also records a high F1-score of 98.3% for healthy individuals, 94.2% for heart disease, and 91.7% for hypertension. In comparison, ORSF-HMM-SDL Fusion shows strong performance with an overall accuracy of 94.8%, sensitivity of 92.3%, and specificity of 96.1%, along with a low error rate of 5.2%. The Kalman Filter Fusion and SVM Based Fusion methods, while effective, show slightly lower performance across most metrics, with Kalman Filter achieving 93.0% accuracy and Deep Learning showing superior performance with 700 data points/min throughput. These findings demonstrate that while deep learning offers the highest overall performance.