Design of a low cost and novel Naive Bayes classifier based NodeMCU web server for fever type detection

Abstract

This study presents a novel NodeMCU based web server to validate cost-effective and smart e-health diagnosis. Proposed work implements a novel Naïve Bayes Classifier for automatic fever type detection as a proof of concept. The application is currently accessible on local network. Network users (patients) are first required to submit eight symptoms e.g. "fatigue", "fever", "chills", "sore throat", "cough", "headache", "muscle pain", and "sneezing" on web portal in form of yes "y" or no "n". Implied Naïve Bayes Classifier engine predicts the probability of occurrence of fever either from flu or common cold as per symptoms provided earlier. The patients are simultaneously diagnosed by a medical practitioner from whom patient wise predictive percentage was received. The probabilistic values were then paired against flu and common cold typed fever patients independently. 22 patients were voluntarily gone through this experiment where p (0.089>0.05) and p (0.068>0.05) were not found to be statistically significant (i.e. no difference between proposed classifier and doctor's diagnosis) for flu and common cold fever types, respectively.