Energy-Efficient IoT-Based Wireless Sensor Network Framework for Detecting Symptomatic and Asymptomatic COVID-19 Patients Using a Fuzzy Logic Approach

Internet of things enabled wireless sensor networks using fuzzy logic approach is used to collect health metrics of individuals, make real-time analyses, and make efficient therapeutic decisions on Covid-19 cases while remotely monitoring the individuals and reducing the virus's consequences. Symptom data for Covid-19 patients is collected using wearable sensors. Data fusion is performed using a fuzzy logic classifier. Preprocessing and filtering of data to produce a verdict are carried out in data fusion. The data with a possible decision is saved in cloud infrastructure, making it accessible to anyone, including users, medical professionals, and local hospitals. The suggested procedure surpasses other similar methods such as logistic regression, decision tree, naïve bayes, k-nearest neighbor, and support vector machine relating to the accurateness, error rate, F-measure, and ROC area according to experimental results. Complicated decisions in the medical industry could be made more successfully with the support of the suggested technique to predict COVID-19 based on fuzzy logic.