Presenting a new model for rapid diagnosis of acute respiratory diseases using machine learning algorithms
Subject Areas : Information Technology in Engineering Design (ITED) Journal
Mehran
Nezami
1
(Computer Engineering Department, University College of Nabi Akram, Tabriz, Iran)
Avaz
Naghipour
2
(Department of Computer Engineering, University College of Nabi Akram, Tabriz- Iran)
Behnam
Safiri Iranagh
3
(Department of Computer Engineering, University College of Nabi Akram, Tabriz, Iran)
Keywords: Machine learning algorithms, epidemics, forecasting,
Abstract :
Corona virus, Severe Acute Respiratory virus and swine flu is a disease caused by acute respiratory syndrome. These viruses require advanced tools to identify dangerous mortality factors with high accuracy due to their immediate spread among humans. Machine learning methods directly address this issue and are essential tools for understanding and guiding public health interventions. In this article, machine learning is used to investigate demographic and clinical significance. The investigated characteristics include age, gender, fever, countries and clinical details such as cough, shortness of breath, etc. Several machine learning algorithms have been implemented and applied on the collected data, the K-Nearest Neighbor algorithm works with the highest accuracy (more than 97%) to predict and select features that correctly represent the status of viruses.
