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

İrem Sena Tekin; Erkan Özhan

Makale detayları

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

Yazarlar:

İrem Sena Tekin | Erkan Özhan

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Bilgisayar Yazılımı | Yazılım Mühendisliği (Diğer)

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.25 Issue 3, pp.510-521

Belge Tipi:

Makale

Yayın Tarihi:

10/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001348295

pISSN:

2149-3367

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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, Türkiyede 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.

Makale detayları

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

Yazarlar:

İrem Sena Tekin | Erkan Özhan

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Bilgisayar Yazılımı | Yazılım Mühendisliği (Diğer)

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.25 Issue 3, pp.510-521

Belge Tipi:

Makale

Yayın Tarihi:

10/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001348295

pISSN:

2149-3367

Pandemic

Life Habits

Artificial Intelligence

Machine Learning

Association Rules

ClassificationPandemi

Yaşam Alışkanlıkları

Yapay Zeka

Makine Öğrenmesi

Birliktelik Kuralları

Sınıflandırma

Sayı

Makale detayları

Yazarlar: İrem Sena Tekin | Erkan Özhan

Yayıncı: Afyon Kocatepe University

Ana Başlıklar: Bilgisayar Yazılımı | Yazılım Mühendisliği (Diğer)

Dergi: Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.25 Issue 3, pp.510-521

Belge Tipi: Makale

Yayın Tarihi: 10/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001348295

pISSN: 2149-3367

Sayı

Yazarlar:

İrem Sena Tekin | Erkan Özhan

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Bilgisayar Yazılımı | Yazılım Mühendisliği (Diğer)

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.25 Issue 3, pp.510-521

Belge Tipi:

Makale

Yayın Tarihi:

10/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001348295

pISSN:

2149-3367