Targeting Vaccine Hesitancy A Data-Driven Approach Using AI and Public Health Data

Bekir Çetintav; Ahmet Yalçın

Makale detayları

Targeting Vaccine Hesitancy A Data-Driven Approach Using AI and Public Health Data

Yazarlar:

Bekir Çetintav | Ahmet Yalçın

Yayıncı:

Burdur Mehmet Akif Ersoy University

Ana Başlıklar:

One Health | Animal Science | Genetics and Biostatistics

Dergi:

Mehmet Akif Ersoy University Journal of Health Sciences Institute Vol.12 Issue 3, pp.24-33

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336147

pISSN:

2148-2837

Anahtar kelimeler:

Özet:

 This study examines H1N1 and seasonal flu vaccination behaviors using machine learning models and explainable artificial intelligence XAI techniques. Utilizing data from the National 2009 H1N1 Influenza Survey, we developed a predictive framework employing models such as CatBoost, XGBoost, and LightGBM. CatBoost outperformed others with an accuracy of 0.696 and an F1 score of 0.688. SHAP Shapley Additive Explanations was used for interpretability, providing both global insights, such as the critical role of doctor recommendations, and local insights, highlighting individual decision factors. Our findings underscore the importance of addressing vaccine skepticism and improving healthcare communication to enhance vaccination uptake. These results contribute to public health strategies aimed at increasing immunization coverage and preparing for future pandemics.

 This study examines H1N1 and seasonal flu vaccination behaviors using machine learning models and explainable artificial intelligence XAI techniques. Utilizing data from the National 2009 H1N1 Influenza Survey, we developed a predictive framework employing models such as CatBoost, XGBoost, and LightGBM. CatBoost outperformed others with an accuracy of 0.696 and an F1 score of 0.688. SHAP Shapley Additive Explanations was used for interpretability, providing both global insights, such as the critical role of doctor recommendations, and local insights, highlighting individual decision factors. Our findings underscore the importance of addressing vaccine skepticism and improving healthcare communication to enhance vaccination uptake. These results contribute to public health strategies aimed at increasing immunization coverage and preparing for future pandemics.

 This study examines H1N1 and seasonal flu vaccination behaviors using machine learning models and explainable artificial intelligence XAI techniques. Utilizing data from the National 2009 H1N1 Influenza Survey, we developed a predictive framework employing models such as CatBoost, XGBoost, and LightGBM. CatBoost outperformed others with an accuracy of 0.696 and an F1 score of 0.688. SHAP Shapley Additive Explanations was used for interpretability, providing both global insights, such as the critical role of doctor recommendations, and local insights, highlighting individual decision factors. Our findings underscore the importance of addressing vaccine skepticism and improving healthcare communication to enhance vaccination uptake. These results contribute to public health strategies aimed at increasing immunization coverage and preparing for future pandemics.

 This study examines H1N1 and seasonal flu vaccination behaviors using machine learning models and explainable artificial intelligence XAI techniques. Utilizing data from the National 2009 H1N1 Influenza Survey, we developed a predictive framework employing models such as CatBoost, XGBoost, and LightGBM. CatBoost outperformed others with an accuracy of 0.696 and an F1 score of 0.688. SHAP Shapley Additive Explanations was used for interpretability, providing both global insights, such as the critical role of doctor recommendations, and local insights, highlighting individual decision factors. Our findings underscore the importance of addressing vaccine skepticism and improving healthcare communication to enhance vaccination uptake. These results contribute to public health strategies aimed at increasing immunization coverage and preparing for future pandemics.

Makale detayları

Targeting Vaccine Hesitancy A Data-Driven Approach Using AI and Public Health Data

Yazarlar:

Bekir Çetintav | Ahmet Yalçın

Yayıncı:

Burdur Mehmet Akif Ersoy University

Ana Başlıklar:

One Health | Animal Science | Genetics and Biostatistics

Dergi:

Mehmet Akif Ersoy University Journal of Health Sciences Institute Vol.12 Issue 3, pp.24-33

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336147

pISSN:

2148-2837

One health

Vaccine hesitancy

Machine learning

Explainable AI

Public health

Biostatistics

Sayı

Makale detayları

Targeting Vaccine Hesitancy A Data-Driven Approach Using AI and Public Health Data

Yazarlar: Bekir Çetintav | Ahmet Yalçın

Yayıncı: Burdur Mehmet Akif Ersoy University

Ana Başlıklar: One Health | Animal Science | Genetics and Biostatistics

Dergi: Mehmet Akif Ersoy University Journal of Health Sciences Institute Vol.12 Issue 3, pp.24-33

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001336147

pISSN: 2148-2837

One health

Vaccine hesitancy

Machine learning

Explainable AI

Public health

Biostatistics

Sayı

Yazarlar:

Bekir Çetintav | Ahmet Yalçın

Yayıncı:

Burdur Mehmet Akif Ersoy University

Ana Başlıklar:

One Health | Animal Science | Genetics and Biostatistics

Dergi:

Mehmet Akif Ersoy University Journal of Health Sciences Institute Vol.12 Issue 3, pp.24-33

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336147

pISSN:

2148-2837