ABSTRACT
COVID-19 has raised the issue of fighting epidemics. We were able to realize that in this fight, countering the spread of the disease was the main goal and we propose to contribute to it. To achieve this, we propose an enriched model of Random Forest (RF) that we called RF EP (EP for Epidemiological Prediction). RF is based on the Forest RI algorithm, proposed by Leo Breiman. Our model (RF EP) is based on a modified version of Forest RI that we called Forest EP. Operations added on Forest RI to obtain Forest EP are as follows: the selection of significant variables, the standardization of data, the reduction in dimensions, and finally the selection of new variables that best synthesize information the algorithm needs. This study uses a data set designed for classification studies to predict whether a patient is suffering from COVID-19 based on the following 11 variables: Country, Age, Fever, Bodypain, Runny_nose, Difficult_in_breathing, Nasal_congestion, Sore_throat, Gender, Severity, and Contact_with_covid_patient. We compared default RF to five other machine learning models: GNB, LR, SVM, KNN, and DT. RF proved to be the best classifier of all with the following metrics: Accuracy (94.9%), Precision (94.0%), Recall (96.6%), and F1 Score (95.2%). Our model, RF EP, produced the following metrics: Accuracy (94.9%), Precision (93.1%), Recall (97.7%), and F1 Score (95.3%). The performance gain by RF EP on the Recall metric compared to default RF allowed us to propose a new model with a better score than default RF in the limitation of the virus propagation on the dataset used in this study. © 2023 by the authors.
ABSTRACT
IoT(Internet of Things) devices are very useful tools for health monitoring, telehealth and teleconsultation. They bring a very positive added value to the different modes of health intervention. Health is one of the most valuable aspects of our lives. In some countries, especially in developing countries, health facilities are not sufficiently developed to meet the needs of the population for primary health care and emergency response. Therefore, there is a need to replace these gaps with IoT devices that will fill the medical deserts. For this reason, in this article, a general review of the literature on IoT devices for health is done. It results in a proposal for a new type of intelligent medical device to assist patients and health workers equipped with sensors for the automatic collection of patients' physiological data in order to make medical consultation more easily accessible to all and at a distance and, on the other hand, to alleviate the shortage of health workers and, moreover, to free doctors from the repetitive tasks they perform at each examination so that they can concentrate on the care to be administered and the psychological care of patients. © 2022 IEEE.
ABSTRACT
Today, the world is facing an unprecedented health crisis due to the pandemic of the coronavirus. Although enormous efforts are being made by all continents to find the appropriate solution, the situation remains worrying. This fact is justified on one hand by the millions of cases with the number of deaths exceeding one and half million on a global scale, and on other hand by the successive waves of lockdown in almost all countries. This situation was exacerbated December 2020 with the arrival in England of a new strain of the more transmissible coronavirus. This great challenge facing all the world's health systems requires actors in the medical domain (health agencies, researchers, and pharmaceutical laboratories, the World Health Organization) to draw and gather knowledge to identify the path for effective treatment. The objective of this work is to provide an operational expert system aimed at helping decision-makers, health workers, and hospital nursing staff in their task of analysis, diagnosis and care of Covid-19 patients. The tool may be extended to other respiratory illnesses.