Version 1
: Received: 23 May 2024 / Approved: 23 May 2024 / Online: 23 May 2024 (16:54:01 CEST)
How to cite:
Asghari Ilani, M.; Moftakhar Tehran, S.; Kavei, A.; Radmehr, A. A Covid-19 Prediction Approach: Prediction of Infectious Disease Probability Using Machine Learning. Preprints2024, 2024051577. https://doi.org/10.20944/preprints202405.1577.v1
Asghari Ilani, M.; Moftakhar Tehran, S.; Kavei, A.; Radmehr, A. A Covid-19 Prediction Approach: Prediction of Infectious Disease Probability Using Machine Learning. Preprints 2024, 2024051577. https://doi.org/10.20944/preprints202405.1577.v1
Asghari Ilani, M.; Moftakhar Tehran, S.; Kavei, A.; Radmehr, A. A Covid-19 Prediction Approach: Prediction of Infectious Disease Probability Using Machine Learning. Preprints2024, 2024051577. https://doi.org/10.20944/preprints202405.1577.v1
APA Style
Asghari Ilani, M., Moftakhar Tehran, S., Kavei, A., & Radmehr, A. (2024). A Covid-19 Prediction Approach: Prediction of Infectious Disease Probability Using Machine Learning. Preprints. https://doi.org/10.20944/preprints202405.1577.v1
Chicago/Turabian Style
Asghari Ilani, M., Ashkan Kavei and Arian Radmehr. 2024 "A Covid-19 Prediction Approach: Prediction of Infectious Disease Probability Using Machine Learning" Preprints. https://doi.org/10.20944/preprints202405.1577.v1
Abstract
Despite the availability of COVID-19 vaccines, the global pandemic remains a significant health challenge. Timely detection of the virus is crucial for effective containment efforts, prompting the exploration of machine learning (ML) models for COVID-19 detection. highlights the ongoing significance of COVID-19 as a global health crisis despite the availability of vaccines. To address the need for timely virus detection, the study explores the efficacy of machine learning (ML) models, including DNN, Voting, Bagging, SVC_rbf, SVC_linear, SVC_polynomial, and SVC_sigmoid, in predicting COVID-19 infection probability. With a dataset comprising 4000 samples split into 3200 for training and 800 for testing, the analysis reveals that DNN and Voting models demonstrate the highest performance, achieving an impressive accuracy of 89%. These results underscore the potential of ML techniques, particularly DNN and ensemble methods like Voting, in facilitating early COVID-19 detection and contributing to effective pandemic management. The study emphasizes the importance of continued research to further refine and optimize ML-based COVID-19 detection systems.
Keywords
Machine Learning; Deep Learning; Random Forest; CatBoost; XGBoost; COVID-19
Subject
Public Health and Healthcare, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
