Remote Disease Diagnosis System

Abstract

In times of pandemic, human-less interaction has become a new normal where diagnostic process has transformed from hospital-centric to home-centric procedure. While hospitals are hard-pressed to treat COVID-19 patients, diagnosis and treatment of other diseases should be carried out in a remote way. Hence, an integrated approach is essential for constant monitoring of a person's health. We propose a system where parameters like height, weight, temperature, pulse rate and moisture of scalp are measured using respective sensors interfaced with Arduino. The measured values are then uploaded to the cloud using a Wi-Fi module. The data uploaded to the cloud are trained using Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) algorithms to predict the prevailing health condition of an individual. By comparing SVM and KNN algorithms, SVM is proved to be more accurate than the KNN algorithm.