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COVID-19’un Yayılma Sürecinde Yaşam Alışkanlıklarının Etkisinin Yapay Zeka ile Analizi

Year 2025, Volume: 25 Issue: 3, 510 - 521

İrem Sena Tekin , Erkan Özhan

Abstract

Pandemiler, toplumların sosyal ve ekonomik yapıları üzerinde önemli etkiler yaratan olaylardır ve bu süreçlerin yayılmasını etkileyen faktörlerin belirlenmesi, krizlerin yönetimi için önemli bilgiler sunar. Bu çalışmada, Türkiye'de COVID-19 pandemisi sırasında enfekte olan ve olmayan bireylerin davranışsal özellikleri ve alışkanlıkları yapay zeka teknikleri, özellikle sınıflandırma ve birliktelik kuralı metodolojileri kullanılarak analiz edilmiştir. Bulgular, Random Committee algoritmasının başarılı bir performans sergilediğini ortaya koymuştur. Ayrıca, öznitelik indirgeme uygulanmış ve RRF algoritmasının %81 doğruluk oranı ve 0.3663 kappa değeri ile daha yüksek performans gösterdiği gözlemlenmiştir. Birliktelik kuralları analizi sonucunda, "Evet" sınıfı (COVID-19 enfekte olan) için 21 kural, "Hayır" sınıfı için ise 2805 kural tespit edilmiştir. Sonuçlar, yalnız yaşayan, günde 1-3 yakın temas yaşayan ve ev dışında 4-6 saat yakın temasta bulunan bireylerin "Evet" sınıfında güçlü birliktelikler sergilediğini göstermektedir. "Hayır" sınıfında ise sık sık seyahat etmekten kaçınan, pandeminin başından beri önemli bir kilo değişikliği yaşamayan, evden çalışan ve bazen açık alanlarda küçük sosyal etkinliklerden kaçınan bireylerin güçlü birliktelikler gösterdiği ortaya çıkmıştır. Sonuç olarak, bu çalışma yaşam tarzı alışkanlıklarının pandemilerin yayılımı üzerindeki etkisini bilimsel bulgularla göstermiş ve bu faktörlerin yapay zeka teknikleri kullanılarak modellenebileceğini ortaya koymuştur.

Keywords

Pandemi Yaşam Alışkanlıkları Yapay Zeka Makine Öğrenmesi Birliktelik Kuralları Sınıflandırma

Ethical Statement

Bu çalışma için etik onay, Tekirdağ Namık Kemal Üniversitesi Bilimsel Araştırma ve Yayın Etik Kurulu’ndan 18/06/2021 tarihli, T2021-648 numaralı ve 8 sayılı karar ile alınmıştır. Bu çalışma, Tekirdağ Namık Kemal Üniversitesi Fen Bilimleri Enstitüsü Bilgisayar Mühendisliği Anabilim Dalı'nda İrem Sena Tekin tarafından, Tez Danışmanı Dr. Öğr. Üyesi Erkan Özhan'ın danışmanlığında hazırlanan ve 30 Ocak 2023 tarihinde savunulan, "Yapay Zeka ile COVID-19’un Yayılma Sürecinde Yaşam Alışkanlıklarının Etkisinin Analizi" başlıklı yüksek lisans tezinden türetilmiştir (Tez No: 10529711).

Thanks

Bu çalışma, Tekirdağ Namık Kemal Üniversitesi Fen Bilimleri Enstitüsü bünyesinde, tez danışmanı Erkan Özhan rehberliğinde İrem Sena Tekin’in yüksek lisans tezinin bir parçasıdır. Yazarlar, Enstitü’ye verdiği destek için ve tüm anket katılımcılarına değerli katkıları için teşekkürlerini sunar.

References

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Analysis of the Impact of Lifestyle Habits on the Spread of COVID-19 Using Artificial Intelligence

Year 2025, Volume: 25 Issue: 3, 510 - 521

İrem Sena Tekin , Erkan Özhan

Abstract

Pandemics are events that significantly impact the social and economic structures of societies, and identifying the factors influencing their spread provides valuable insights for managing these crises. This study analyzed the behavioral characteristics and habits of individuals infected and not infected during the COVID-19 pandemic in Türkiye using artificial intelligence techniques, specifically classification and association rule methodologies. The findings revealed that the Random Committee algorithm performed effectively. Additionally, feature reduction was applied, and the RRF algorithm achieved higher performance with 81% accuracy and a kappa value of 0.3663. In the subsequent analysis of association rules, 21 rules were identified for the "Yes" class (infected with COVID-19), while 2805 rules were found for the "No" class. The results indicated that individuals who live alone, have 1-3 close contacts per day, and spend 4-6 hours in close contact outside the home exhibited strong associations in the "Yes" class. For the "No" class, individuals who frequently avoid travel, have had no significant weight change since the start of the pandemic, work from home, and sometimes avoid small social gatherings in open spaces showed strong associations. In conclusion, the study scientifically demonstrated that lifestyle habits impact the transmission and spread of pandemics and that these factors can be modeled using artificial intelligence techniques.

Keywords

Pandemic Life Habits Artificial Intelligence Machine Learning Association Rules Classification

Ethical Statement

Ethical approval for this study was obtained from the Scientific Research and Publication Ethics Committee of Tekirdağ Namık Kemal University, with the decision dated 18/06/2021, numbered T2021-648, and decision number 8. This study is derived from a master's thesis (thesis number: 10529711) titled "Analysis of the Effect of Life Habits in the Spreading Process of COVID-19 with Artificial Intelligence," defended on January 30, 2023, under the supervision of Assist. Prof. Erkan Özhan by İrem Sena Tekin in the Department of Computer Engineering at Tekirdağ Namık Kemal University, Institute of Natural and Applied Sciences.

Thanks

This study is part of the Master of Science thesis by İrem Sena Tekin, conducted in the Department of Computer Engineering within the Institute of Natural and Applied Sciences at Tekirdağ Namık Kemal University, under the supervision of thesis advisor Erkan Özhan. The authors would like to thank the Institute for its support and all survey participants for their valuable contributions.

References

  • Adamo, J.-M. (2001). Data Mining for Association Rules and Sequential Patterns. Springer New York. https://doi.org/10.1007/978-1-4613-0085-4
  • Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, 487–499.
  • Alakus, T. B., & Turkoglu, I. (2020). Comparison of deep learning approaches to predict COVID-19 infection. Chaos, Solitons & Fractals, 140, 110120. https://doi.org/10.1016/j.chaos.2020.110120
  • Älgå, A., Eriksson, O., & Nordberg, M. (2020). Analysis of Scientific Publications During the Early Phase of the COVID-19 Pandemic: Topic Modeling Study. Journal of Medical Internet Research, 22(11), e21559. https://doi.org/10.2196/21559
  • Asia, S. (2022). Americas Covid-19 South-East Asia. 1–5. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Brinati, D., Campagner, A., Ferrari, D., Locatelli, M., Banfi, G., & Cabitza, F. (2020). Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study. Journal of Medical Systems, 44(8), 135. https://doi.org/10.1007/s10916-020-01597-4
  • De Haas, M., Faber, R., & Hamersma, M. (2020). How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands. Transportation Research Interdisciplinary Perspectives, 6, 100150. https://doi.org/10.1016/j.trip.2020.100150
  • Duval, K. L., & Collaborator, S. B. (2020). iCARE Collaborator Documents. https://doi.org/10.17605/OSF.IO/NSWCM
  • Epsi, N. J., Chenoweth, J. G., Blair, P. W., Lindholm, D. A., Ganesan, A., Lalani, T., Smith, A., Mody, R. M., Jones, M. U., Colombo, R. E., Colombo, C. J., Schofield, C., Ewers, E. C., Larson, D. T., Berjohn, C. M., Maves, R. C., Fries, A. C., Chang, D., Wyatt, A., … Richard, S. A. (2024). Precision Symptom Phenotyping Identifies Early Clinical and Proteomic Predictors of Distinct COVID-19 Sequelae. The Journal of Infectious Diseases, jiae318. https://doi.org/10.1093/infdis/jiae318
  • Fernández, R. R., Martín De Diego, I., Aceña, V., Fernández-Isabel, A., & Moguerza, J. M. (2020). Random forest explainability using counterfactual sets. Information Fusion, 63, 196–207. https://doi.org/10.1016/j.inffus.2020.07.001
  • Grekousis, G., Feng, Z., Marakakis, I., Lu, Y., & Wang, R. (2022). Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach. Health & Place, 74, 102744. https://doi.org/10.1016/j.healthplace.2022.102744
  • Işik, K., & Kapan Ulusoy, S. (2021). Metal Sektöründe üretim sürelerine etki eden faktörlerin veri madenciliği yöntemleriyle tespit edilmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(4), 1949–1962. https://doi.org/10.17341/gazimmfd.736659
  • Israfil, S. M. H., Sarker, Md. M. R., Rashid, P. T., Talukder, A. A., Kawsar, K. A., Khan, F., Akhter, S., Poh, C. L., Mohamed, I. N., & Ming, L. C. (2021). Clinical Characteristics and Diagnostic Challenges of COVID−19: An Update From the Global Perspective. Frontiers in Public Health, 8, 567395. https://doi.org/10.3389/fpubh.2020.567395 Jana, R. K., Ghosh, I., Das, D., & Dutta, A. (2021). Determinants of electronic waste generation in Bitcoin network: Evidence from the machine learning approach. Technological Forecasting and Social Change, 173, 121101. https://doi.org/10.1016/j.techfore.2021.121101
  • Kartsonaki, C., Baillie, J. K., Barrio, N. G., Baruch, J., Beane, A., Blumberg, L., Bozza, F., Broadley, T., Burrell, A., Carson, G., Citarella, B. W., Dagens, A., Dankwa, E. A., Donnelly, C. A., Dunning, J., Elotmani, L., Escher, M., Farshait, N., Goffard, J.-C., … Zucman, D. (2023). Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19. International Journal of Epidemiology, 52(2), 355–376. https://doi.org/10.1093/ije/dyad012
  • Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321–332. https://doi.org/10.1038/nrg3920
  • Liemohn, M. W., Shane, A. D., Azari, A. R., Petersen, A. K., Swiger, B. M., & Mukhopadhyay, A. (2021). RMSE is not enough: Guidelines to robust data-model comparisons for magnetospheric physics. Journal of Atmospheric and Solar-Terrestrial Physics, 218, 105624. https://doi.org/10.1016/j.jastp.2021.105624
  • Linden, T., Hanses, F., Domingo-Fernández, D., DeLong, L. N., Kodamullil, A. T., Schneider, J., Vehreschild, M. J. G. T., Lanznaster, J., Ruethrich, M. M., Borgmann, S., Hower, M., Wille, K., Feldt, T., Rieg, S., Hertenstein, B., Wyen, C., Roemmele, C., Vehreschild, J. J., Jakob, C. E. M., … Fröhlich, H. (2021). Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases. Artificial Intelligence in the Life Sciences, 1, 100020. https://doi.org/10.1016/j.ailsci.2021.100020
  • Mallah, S. I., Ghorab, O. K., Al-Salmi, S., Abdellatif, O. S., Tharmaratnam, T., Iskandar, M. A., Sefen, J. A. N., Sidhu, P., Atallah, B., El-Lababidi, R., & Al-Qahtani, M. (2021). COVID-19: Breaking down a global health crisis. Annals of Clinical Microbiology and Antimicrobials, 20(1), 35. https://doi.org/10.1186/s12941-021-00438-7
  • Moore, K. A., Lipsitch, M., Barry, J. M., & Osterholm, M. T. (2020). COVID‐19: The CIDRAP Viewpoint. Center of Infectious Diseases Research and Policy, 1–9.
  • Moraglio, A., Di Chio, C., & Poli, R. (2007). Geometric Particle Swarm Optimisation. In M. Ebner, M. O’Neill, A. Ekárt, L. Vanneschi, & A. I. Esparcia-Alcázar (Eds.), Genetic Programming (Vol. 4445, pp. 125–136). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_12
  • Muralidharan, N., Gupta, S., Prusty, M. R., & Tripathy, R. K. (2022). Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network. Applied Soft Computing, 119, 108610. https://doi.org/10.1016/j.asoc.2022.108610
  • Niranjan, A., Prakash, A., Veena, N., Geetha, M., Shenoy, P. D., & Venugopal, K. R. (2018). EBJRV: An Ensemble of Bagging, J48 and Random Committee by Voting for Efficient Classification of Intrusions. WIECON-ECE 2017 - IEEE International WIE Conference on Electrical and Computer Engineering 2017, 51–54. https://doi.org/10.1109/WIECON-ECE.2017.8468876
  • Papi, R., Attarchi, S., Darvishi Boloorani, A., & Neysani Samany, N. (2022). Knowledge discovery of Middle East dust sources using Apriori spatial data mining algorithm. Ecological Informatics, 72, 101867. https://doi.org/10.1016/j.ecoinf.2022.101867
  • Rahimi, I., Chen, F., & Gandomi, A. H. (2023). A review on COVID-19 forecasting models. Neural Computing and Applications, 35(33), 23671–23681. https://doi.org/10.1007/s00521-020-05626-8
  • Sabherwal, A. K., Sood, A., & Shah, M. A. (2024). Evaluating mathematical models for predicting the transmission of COVID-19 and its variants towards sustainable health and well-being. Discover Sustainability, 5(1), 38. https://doi.org/10.1007/s43621-024-00213-6
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Details

Primary Language English
Subjects Computer Software, Software Engineering (Other)
Journal Section Articles
Authors

İrem Sena Tekin 0000-0001-8966-7802

Erkan Özhan 0000-0002-3971-2676

Early Pub Date May 22, 2025
Publication Date
Submission Date September 14, 2024
Acceptance Date December 9, 2024
Published in Issue Year 2025 Volume: 25 Issue: 3

Cite

APA Tekin, İ. S., & Özhan, E. (2025). Analysis of the Impact of Lifestyle Habits on the Spread of COVID-19 Using Artificial Intelligence. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 25(3), 510-521.
AMA Tekin İS, Özhan E. Analysis of the Impact of Lifestyle Habits on the Spread of COVID-19 Using Artificial Intelligence. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. May 2025;25(3):510-521.
Chicago Tekin, İrem Sena, and Erkan Özhan. “Analysis of the Impact of Lifestyle Habits on the Spread of COVID-19 Using Artificial Intelligence”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 25, no. 3 (May 2025): 510-21.
EndNote Tekin İS, Özhan E (May 1, 2025) Analysis of the Impact of Lifestyle Habits on the Spread of COVID-19 Using Artificial Intelligence. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 25 3 510–521.
IEEE İ. S. Tekin and E. Özhan, “Analysis of the Impact of Lifestyle Habits on the Spread of COVID-19 Using Artificial Intelligence”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 25, no. 3, pp. 510–521, 2025.
ISNAD Tekin, İrem Sena - Özhan, Erkan. “Analysis of the Impact of Lifestyle Habits on the Spread of COVID-19 Using Artificial Intelligence”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 25/3 (May 2025), 510-521.
JAMA Tekin İS, Özhan E. Analysis of the Impact of Lifestyle Habits on the Spread of COVID-19 Using Artificial Intelligence. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2025;25:510–521.
MLA Tekin, İrem Sena and Erkan Özhan. “Analysis of the Impact of Lifestyle Habits on the Spread of COVID-19 Using Artificial Intelligence”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 25, no. 3, 2025, pp. 510-21.
Vancouver Tekin İS, Özhan E. Analysis of the Impact of Lifestyle Habits on the Spread of COVID-19 Using Artificial Intelligence. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2025;25(3):510-21.
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