Londra'daki Sağlık Önlemlerini Makine Öğrenimi Odaklı Veri Madenciliği ile Analiz Etmede Bir 21. Yüzyıl Yaklaşımı
Öz
Geçmişte olduğu gibi günümüzde de koruyucu tedaviler ve sağlık politikaları birçok hastalık, pandemi, salgın gibi tıbbi olgularla mücadelede önemli rol oynamaktadır. Bu tarz yaklaşımlar, sağlık odaklı bazı olumsuz sonuçları erken aşamalarda önleyebilmekte ve liderlere, tıp uzmanlarına sağlık sorunlarıyla ilişkili riskleri yönetmede avantaj sağlayabilmektedir. Genellikle erken uyarı sistemlerinin etkin kullanımı, keşifsel ve doğrulayıcı anlayış için geçmiş verilerin analizi bu bağlamda çeşitli avantajlar sağlayabilir. Bu çalışmada, makine öğrenmesine dayalı veri madenciliği yardımıyla benzer olguları anlamak için tarihsel bir veri analizi uygulanmıştır. Bu yaklaşımlar kapsamında kümeleme ve sınıflandırma teknikleri ile algoritma performansları ve kuralları değerlendirilmiştir.
Anahtar Kelimeler
Sağlık Kısıtlamaları Sağlık Önlemleri Covid-19 Pandemi Salgın Kümeleme Sınıflandırma Veri Madenciliği Makine Öğrenimi Kantitatif Analiz Denetimli Öğrenme Denetimsiz Öğrenme
Kaynakça
- https://www.health.harvard.edu/diseases-and-conditions/preventing-the-spread-of-the-coronavirus
- https://www.health.harvard.edu/diseases-and-conditions/coronavirus-resource-center
- SWOT Analysis: Discover New Opportunities, Manage and Eliminate Threats". www.mindtools.com. 2016. Retrieved 24 February 2018.
- Sammut-Bonnici, Tanya & Galea, David. (2015). SWOT Analysis. 10.1002/9781118785317.weom120103.
- Satoshi Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System,2008
- Águila, R.D.M., Ramírez, G.A., 2013. Series: basic statistics for busy clinicians. Allergol Immunopathol. 42 (5), pp. 485-492.
- Blackmore, K., Bossomaier, T., 2002. Comparison of See5 and J48.PART algorithms for missing persons profiling. International Conference on Information Technology and Applications
- Frank E. and Witten I.H. (1998). Generating Accurate Rule Sets Without Global Optimization. In Shavlik, J., ed., Machine Learning: Proceedings of the Fifteenth International Conference, Morgan Kaufmann Publishers.
- Frank E. and Witten I.H. (2000). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers: San Francisco, CA.
- Lemeshow S., Hosmer D.W., Klar J. & Lwanga S.K., 1990. Adequacy of sample size in health studies. Chichester: John Wiley and Sons.
- Merriam-Webster, 2020. https://www. merriam-webster.com [date accessed 9 August 2020]
- Ramchoun, H. r., Idrissi, M. m., Ghanou, Y. y., & Ettaouil, M. m. (2017). New Modeling of Multilayer Perceptron Architecture Optimization with Regularization: An Application to Pattern Classification. IAENG International Journal of Computer Science, 44(3), 261-269.
- Rosenblatt, F., & Cornell Aeronautical Laboratory. (1958). The perceptron: A theory of statistical separability in cognitive systems (Project Para). Buffalo, N.Y: Cornell Aeronautical Laboratory.
- Shearer, C., 2000 The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing, 5, 13-22.
- Simoudis, E. (1996). Reality Check for Data Mining. IEEE EXPERT, 11(5), pp.26-33
- Cohen, W. (1995). Fast effective rule induction. In A. Prieditis and S. Russell (eds.), Proceedings of the 12th International Conference on Machine Learning, Lake Tahoe, CA, pp.115-123.
- Saravanan, N., Gayathri V., 2018. Performance and classification evaluation of J48 algorithm and Kendall's based J48 algorithm (KNJ48). International Journal of Computer Trends and Technology
- Sasaki M., Kita K., 1998. Rule based text categorization using hierarchical categories, IEEE
- Surveymonkey, 2017. https://www.surveymonkey.com/mp/sample-size-calculator/ [date accessed 28 October 2017]
- Taniguchi M., Haft M., Hollm´en J., and Tresp V. (1998). Fraud detection in communications networks using neural and probabilistic methods. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'98), Volume II, pp. 1241-1244.
- Venkatesan, E. V., 2015. Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification. Indian Journal of Science and Technology.
- Yavuz Ö., 2019, A data mining approach for desire and intention to participate in virtual communities. International Journal of Electrical and Computer Engineering, 9(5).
- Ławrynowicz, A., Tresp, V., 2014. Introducing Machine Learning. Perspectives on Ontology Learning. AKA Heidelberg /IOS Press.
- Thomas, M., 2012. Root Mean Square Error Compared to, and Contrasted with, Standard Deviation. Surveying and Land Information Science, 72.
- Ławrynowicz, A., Tresp, V., 2014. Introducing Machine Learning. Perspectives on Ontology Learning. AKA Heidelberg /IOS Press.
- Thomas, M., 2012. Root Mean Square Error Compared to, and Contrasted with, Standard Deviation. Surveying and Land Information Science, 72.
- Karahoca D., Karahoca A., Yavuz Ö., 2013. An early warning system approach for the identification of currency crises with data mining techniques. Neural Computing and Applications, 23(7-8)
- Rasmussen, C. E.; Williams, C. K. I. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning); The MIT Press: 2005.
- Anil Rajput, 2011 J48 and JRIP Rules for E-Governance Data, International Journal of Computer Science and Security (IJCSS), 5(2)
- Conkright, Todd. (2015). Using the Four Functions of Management for Sustainable Employee Engagement. Performance Improvement. 54. 10.1002/pfi.21506.
- Ajzen, I. & Fishbein, M., 1980. Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice Hall.
- Yavuz, Ö., 2018. Marketing Implications Of Participative Behavior In Virtual Communities, Bahcesehir University Graduate School of Social Sciences, Management-Marketing Program, Istanbul
- Yavuz, Ö., 2009. An early warning system approach for the identification of currency crises, Bahcesehir University Graduate School of Sciences, Computer Engineering Graduate Program, Istanbul
- Yavuz, Ö. (2021). A Public Perceptions Analysis With Data Mining Algorithms, 2. International “Başkent” Congress On Physical, Social and Health Sciences, Ankara
- Yavuz, Ö. (2021). A Data Mining Analysis of Coronavirus Cases and Vaccinations in The City of London. Astana, Ankara.
A 21st Century Approach in Analysing Health Precautions in London with Machine Learning Driven Data Mining
Öz
As in the past, today preventive treatments and health policies constitute an important role in combatting with several diseases, medical phenomenon like pandemies or epidemies. These approaches can prevent several health focused negative consequences in early stages or can give leaders and medical professionals advantage in managing risks associated with health concerns. Usually effective usage of early warning systems, analysis of historical data for exploratory and confirmatory understanding may provide several advantages in this context. In this study a historical data analysis has been applied to understand similar phenomena with the help of machine learning driven data mining. Clustering and classification performances and rules generated by these approaches have also been assessed.
Anahtar Kelimeler
Health Restriction Health Precaution Covid-19 Pandemy Epidemy Clustering Classification Data Mining Machine Learning Quantitative Analysis Supervised Learning Unsupervised Learning
Kaynakça
- https://www.health.harvard.edu/diseases-and-conditions/preventing-the-spread-of-the-coronavirus
- https://www.health.harvard.edu/diseases-and-conditions/coronavirus-resource-center
- SWOT Analysis: Discover New Opportunities, Manage and Eliminate Threats". www.mindtools.com. 2016. Retrieved 24 February 2018.
- Sammut-Bonnici, Tanya & Galea, David. (2015). SWOT Analysis. 10.1002/9781118785317.weom120103.
- Satoshi Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System,2008
- Águila, R.D.M., Ramírez, G.A., 2013. Series: basic statistics for busy clinicians. Allergol Immunopathol. 42 (5), pp. 485-492.
- Blackmore, K., Bossomaier, T., 2002. Comparison of See5 and J48.PART algorithms for missing persons profiling. International Conference on Information Technology and Applications
- Frank E. and Witten I.H. (1998). Generating Accurate Rule Sets Without Global Optimization. In Shavlik, J., ed., Machine Learning: Proceedings of the Fifteenth International Conference, Morgan Kaufmann Publishers.
- Frank E. and Witten I.H. (2000). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers: San Francisco, CA.
- Lemeshow S., Hosmer D.W., Klar J. & Lwanga S.K., 1990. Adequacy of sample size in health studies. Chichester: John Wiley and Sons.
- Merriam-Webster, 2020. https://www. merriam-webster.com [date accessed 9 August 2020]
- Ramchoun, H. r., Idrissi, M. m., Ghanou, Y. y., & Ettaouil, M. m. (2017). New Modeling of Multilayer Perceptron Architecture Optimization with Regularization: An Application to Pattern Classification. IAENG International Journal of Computer Science, 44(3), 261-269.
- Rosenblatt, F., & Cornell Aeronautical Laboratory. (1958). The perceptron: A theory of statistical separability in cognitive systems (Project Para). Buffalo, N.Y: Cornell Aeronautical Laboratory.
- Shearer, C., 2000 The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing, 5, 13-22.
- Simoudis, E. (1996). Reality Check for Data Mining. IEEE EXPERT, 11(5), pp.26-33
- Cohen, W. (1995). Fast effective rule induction. In A. Prieditis and S. Russell (eds.), Proceedings of the 12th International Conference on Machine Learning, Lake Tahoe, CA, pp.115-123.
- Saravanan, N., Gayathri V., 2018. Performance and classification evaluation of J48 algorithm and Kendall's based J48 algorithm (KNJ48). International Journal of Computer Trends and Technology
- Sasaki M., Kita K., 1998. Rule based text categorization using hierarchical categories, IEEE
- Surveymonkey, 2017. https://www.surveymonkey.com/mp/sample-size-calculator/ [date accessed 28 October 2017]
- Taniguchi M., Haft M., Hollm´en J., and Tresp V. (1998). Fraud detection in communications networks using neural and probabilistic methods. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'98), Volume II, pp. 1241-1244.
- Venkatesan, E. V., 2015. Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification. Indian Journal of Science and Technology.
- Yavuz Ö., 2019, A data mining approach for desire and intention to participate in virtual communities. International Journal of Electrical and Computer Engineering, 9(5).
- Ławrynowicz, A., Tresp, V., 2014. Introducing Machine Learning. Perspectives on Ontology Learning. AKA Heidelberg /IOS Press.
- Thomas, M., 2012. Root Mean Square Error Compared to, and Contrasted with, Standard Deviation. Surveying and Land Information Science, 72.
- Ławrynowicz, A., Tresp, V., 2014. Introducing Machine Learning. Perspectives on Ontology Learning. AKA Heidelberg /IOS Press.
- Thomas, M., 2012. Root Mean Square Error Compared to, and Contrasted with, Standard Deviation. Surveying and Land Information Science, 72.
- Karahoca D., Karahoca A., Yavuz Ö., 2013. An early warning system approach for the identification of currency crises with data mining techniques. Neural Computing and Applications, 23(7-8)
- Rasmussen, C. E.; Williams, C. K. I. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning); The MIT Press: 2005.
- Anil Rajput, 2011 J48 and JRIP Rules for E-Governance Data, International Journal of Computer Science and Security (IJCSS), 5(2)
- Conkright, Todd. (2015). Using the Four Functions of Management for Sustainable Employee Engagement. Performance Improvement. 54. 10.1002/pfi.21506.
- Ajzen, I. & Fishbein, M., 1980. Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice Hall.
- Yavuz, Ö., 2018. Marketing Implications Of Participative Behavior In Virtual Communities, Bahcesehir University Graduate School of Social Sciences, Management-Marketing Program, Istanbul
- Yavuz, Ö., 2009. An early warning system approach for the identification of currency crises, Bahcesehir University Graduate School of Sciences, Computer Engineering Graduate Program, Istanbul
- Yavuz, Ö. (2021). A Public Perceptions Analysis With Data Mining Algorithms, 2. International “Başkent” Congress On Physical, Social and Health Sciences, Ankara
- Yavuz, Ö. (2021). A Data Mining Analysis of Coronavirus Cases and Vaccinations in The City of London. Astana, Ankara.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Aralık 2021 |
| Yayımlandığı Sayı | Yıl 2021 Sayı: 32 |
- Makale Dosyaları