Authors : Abdul SAMAD; Muhammed Kürsad UÇAR

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/ycykz8b7

Scribd : https://tinyurl.com/yn46t4am

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL1493

Abstract : The COVID-19 pandemic has resulted in a significant global health crisis, claiming over 6.3 million lives. Rapid and accurate detection of COVID-19 symptoms is essential for effective public health responses. This study utilizes machine learning algorithms to enhance the speed and accuracy of COVID-19 diagnosis based on symptom data. By employing the Spearman feature selection algorithm, we identified the most predictive features, thereby improving model performance and reducing the number of features required. The decision tree algorithm proved to be the most effective, achieving an accuracy of 98.57%, perfect sensitivity of 1, and high specificity of 0.97. Our results indicate that combining various symptoms with AI-based machine learning techniques can accurately detect COVID-19 patients. These findings surpass previous studies, demonstrating superior performance across multiple evaluations. The integration of feature selection with advanced machine learning models offers a practical and efficient tool for early COVID-19 diagnosis, improving patient management and public health responses. This approach holds significant promise for enhancing pandemic management and healthcare delivery.

Keywords : Covid-19, Machine Learning, Artificial Intelligence, Spearman Algorithm, Decision Tree Algorithm.

References :

1. Y. Zoabi, S. Deri-Rozov, and N. Shomron, “Machine learning-based prediction of COVID-19 diagnosis based on symptoms,” NPJ Digit Med, vol. 4, no. 1, Dec. 2021, doi: 10.1038/s41746-020-00372-6.
2. S. Bu et al., “An optimized machine learning model for predicting hospitalization for COVID-19 infection in the maintenance dialysis population,” Comput Biol Med, vol. 165, Oct. 2023, doi: 10.1016/J.COMPBIOMED.2023.107410.
3. V. V. Khanna, K. Chadaga, N. Sampathila, S. Prabhu, and P. Rajagopala Chadaga, “A machine learning and explainable artificial intelligence triage-prediction system for COVID-19,” Decision Analytics Journal, vol. 7, Jun. 2023, doi: 10.1016/j.dajour.2023.100246.
4. S. Guhathakurata, S. Kundu, A. Chakraborty, and J. S. Banerjee, “A novel approach to predict COVID-19 using support vector machine,” Data Science for COVID-19 Volume 1: Computational Perspectives, pp. 351–364, Jan. 2021, doi: 10.1016/B978-0-12-824536-1.00014-9.
5. N. S. ÖZEN, S. SARAÇ, and M. KOYUNCU, “COVID-19 Vakalarının Makine Öğrenmesi Algoritmaları ile Tahmini: Amerika Birleşik Devletleri Örneği,” European Journal of Science and Technology, Jan. 2021, doi: 10.31590/ejosat.855113.
6. M. Krämer, M. Ingwersen, U. Teichgräber, and F. Güttler, “Added value of chest CT in a machine learning-based prediction model to rule out COVID-19 before inpatient admission: A retrospective university network study,” Eur J Radiol, vol. 163, Jun. 2023, doi: 10.1016/J.EJRAD.2023.110827.
7. M. Haucke, A. Heinz, S. Liu, and S. Heinzel, “The Impact of COVID-19 Lockdown on Daily Activities, Cognitions, and Stress in a Lonely and Distressed Population: Temporal Dynamic Network Analysis,” J Med Internet Res, vol. 24, no. 3, Mar. 2022, doi: 10.2196/32598.
8. Shobhika, P. Kumar, and S. Chandra, “Prediction and comparison of psychological health during COVID-19 among Indian population and Rajyoga meditators using machine learning algorithms,” Procedia Comput Sci, vol. 218, pp. 697–705, 2023, doi: 10.1016/J.PROCS.2023.01.050.
9. F. M. Albagmi, A. Alansari, D. S. Al Shawan, H. Y. AlNujaidi, and S. O. Olatunji, “Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach,” Inform Med Unlocked, vol. 28, p. 100854, Jan. 2022, doi: 10.1016/J.IMU.2022.100854.
10. N. Yalçın and S. Ünaldı, “Symptom Based COVID-19 Prediction Using Machine Learning and Deep Learning Algorithms,” APA, 2022.
11. M. E. Elkin and X. Zhu, “A machine learning study of COVID-19 serology and molecular tests and predictions,” Smart Health, vol. 26, Dec. 2022, doi: 10.1016/j.smhl.2022.100331.
12. M. A. Arshed, W. Qureshi, M. U. G. Khan, and M. A. Jabbar, “Symptoms Based Covid-19 Disease Diagnosis Using Machine Learning Approach,” in 4th International Conference on Innovative Computing, ICIC 2021, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/ICIC53490.2021.9692986.
13. H. Mir et al., “Article ID 7713939, 16 pages,” Hindawi Journal of Healthcare Engineering, vol. 2022, p. page, 2022, doi: 10.1155/2023/9768467.
14. S. H. Kassania, P. H. Kassanib, M. J. Wesolowskic, K. A. Schneidera, and R. Detersa, “Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach,” Biocybern Biomed Eng, vol. 41, no. 3, pp. 867–879, Jul. 2021, doi: 10.1016/j.bbe.2021.05.013.
15. M. Pal et al., “Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach,” Biomed Res Int, vol. 2022, 2022, doi: 10.1155/2022/3113119.
16. P. Sumari, S. Jamal Syed, L. Abualigah, and L. Abualigah Aligah, “A Novel Deep Learning Pipeline Architecture based on CNN to Detect Covid-19 in Chest X-ray Images,” 2021.
17. M. Laatifi et al., “Machine learning approaches in Covid-19 severity risk prediction in Morocco,” J Big Data, vol. 9, no. 1, Dec. 2022, doi: 10.1186/s40537-021-00557-0.
18. C. N. Villavicencio et al., “COVID-19 Prediction Applying Supervised Machine Learning Algorithms with Comparative Analysis Using WEKA,” Algorithms 2021, Vol. 14, Page 201, vol. 14, no. 7, p. 201, Jun. 2021, doi: 10.3390/A14070201.
19. “Symptoms and COVID Presence (May 2020 data).” Accessed: Jul. 24, 2024. [Online]. Available: https://www.kaggle.com/datasets/hemanthhari/symptoms-and-covid-presence
20. M. K. Uçar, “Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm,” Journal of Intelligent Systems: Theory and Applications, vol. 2, no. 1, pp. 7–12, Jan. 2019, doi: 10.38016/JISTA.498799.
21. M. K. Uçar, “Classification Performance-Based Feature Selection Algorithm for Machine Learning: P-Score,” IRBM, vol. 41, no. 4, pp. 229–239, Aug. 2020, doi: 10.1016/J.IRBM.2020.01.006.
22. A. Samad and E. S. Aydı, “Rapid Alzheimer’s Disease Diagnosis Using Advanced Artificial Intelligence Algorithms,” Int J Innov Sci Res Technol, vol. 9, no. 6, 2024, doi: 10.38124/ijisrt/IJISRT24JUN1915.
23. H. H. Patel and P. Prajapati, “Study and Analysis of Decision Tree Based Classification Algorithms,” International Journal of Computer Sciences and Engineering, vol. 6, no. 10, pp. 74–78, Oct. 2018, doi: 10.26438/IJCSE/V6I10.7478.
24. L. Breiman, “Random forests,” Mach Learn, vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.
25. “(PDF) COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection.” Accessed: Jul. 14, 2024. [Online]. Available: https://www.researchgate.net/publication/340271344_COVID-19_Screening_on_Chest_X-ray_Images_Using_Deep_Learning_based_Anomaly_Detection
26. “(PDF) Diagnosis of COVID-19 from X-rays Using Combined CNN-RNN Architecture with Transfer Learning.” Accessed: Jul. 14, 2024. [Online]. Available: https://www.researchgate.net/publication/344004449_Diagnosis_of_COVID-19_from_X-rays_Using_Combined_CNN-RNN_Architecture_with_Transfer_Learning
27. L. Wang, Z. Q. Lin, and A. Wong, “COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images,” preimpresión de arXiv arXiv, pp. 1–12, Mar. 2020, Accessed: Jul. 14, 2024. [Online]. Available: https://arxiv.org/abs/2003.09871v4