ABSTRACT
Detecting COVID-19 in the early time can save lives and reduce the cost of huge pressure on healthcare centers. Many machine and deep learning models have been proposed by researchers to detect and diagnose COVID-19 based on chest X-rays. However, we need to know which of those models is more effective and efficient. This paper presents a comparative study between adaptive fuzzy neural network (AFNN) and convolutional neural network (CNN) in classifying COVID-19 using chest X-rays. We present the experimental results showing the comparative performance measures with respect to the size of available dataset. We also present the relative advantage of each family of neural network in accuracy, precision, recall, F1score, and the computation time. © 2022 IEEE.
ABSTRACT
Amidst the current covid-19 pandemic situation continuous health monitoring becomes important. In this work, we propose a low cost portable healthcare module which helps in tracking a patient's health conditions using various parameters such as heart rate, carbon dioxide exhalation, body temperature and electrical heart recording (ECG). This monitoring can be done autonomously without the presence of a doctor. This module is helpful in the health monitoring of patients who are in quarantine, or under treatment in a hospital. It can also be used for the health monitoring of elderly and diabled patients. In this work, we also compare some of the existing modules and draw a comparison. In addition to that we also compare different machine learning algorithms used for prediction of asthma. Our results for different algorithms have been quantified and we found that using K neighbors we got the maximum score of 87%. © 2021 IEEE.