Makine Öğrenmesi Yaklaşımlarını Kullanarak Salgınları Erken Evrede Tespit Etme Alanındaki Eğilimler

Yıl 2021, Cilt: 14 Sayı: 4, 355 - 366, 31.10.2021

Ali Şenol , Yavuz Canbay , Mahmut Kaya

Öz

Tüm dünyayı etkisi altına alan COVID-19, salgınları erken dönemde tespit etmeye çalışan çalışmaların önemini ortaya koymaktadır. Herhangi bir salgın erken aşamada tespit edilebilirse, hastalığa yakalanan kişi sayısını azaltabilir ve gerekli tedavi daha erken sürede bulunabilir ve ek olarak tedavi masrafları da azaltılabilir. Salgınların erken aşamada tespit edilmesini sağlayan en önemli veri işleme yaklaşımlarından makine öğrenmesi, yeni gelen verileri, olayı veya durumu tahmin etmek için matematiksel modelleri ve istatistiksel yöntemleri kullanır. Makine öğrenmesi yaklaşımlarıyla, tıbbi veriler analiz edilerek ve işlenerek hastalıklar hakkında tahminlerde bulunulabilir. Çünkü daha önce toplanan hasta verileri, makine öğrenmesi yöntemleri kullanarak hastalıkların teşhis edilmesine imkân sağlayabilir. Hastalıkların yanı sıra, daha önce toplanan veriler kullanılarak salgınlar hakkında da tahminlerde bulunulabilir. Daha önce ortaya çıkan salgınların yeniden ortaya çıkışını tahmin etmek için denetimli öğrenme yaklaşımları olan Naive Bayes, Destek Vektör Makineleri (DVM), Karar Ağaçları (KA), Rastgele Orman (RO) ve Yapay Sinir Ağları (YSA) gibi birçok yaklaşım olsa da, temel bileşenler ve kümeleme analizi gibi denetimsiz öğrenme yaklaşımları da kullanılarak daha önce benzeri görülmemiş salgınlar tespit edilebilir. Bu çalışmada, bu alanda çalışmak isteyen araştırmacılara ışık tutmak amacıyla salgınları tespit etmeye yönelik geliştirilmiş olan makine öğrenmesi yaklaşımlarının ayrıntılı bir analizi sunulmaktadır.

Anahtar Kelimeler

makine öğrenmesi, salgın erken tespiti, pandemi, tahmin

Trends in Outbreak Detection in Early Stage by Using Machine Learning Approaches

Öz

COVID-19 pandemic affecting the whole world, reveals the importance of the studies that trying to detect the outbreaks in early stage. If any outbreak can be detected in an early stage, the number of infected people can be reduced, the necessary treatment can be found and treatment expenses can be also reduced. The most important data processing approaches enabling to detect outbreaks in an early stage are machine learning approaches, which use mathematical models and statistical background. With machine learning techniques, medical data can be analyzed and processed to make predictions of illnesses. Because, previously collected patient datasets help to perform these predictions. Beside illnesses, outbreaks can be also predicted by using these collected datasets. Machine learning techniques enable us to process labelled and unlabelled datasets with the help of supervised and unsupervised approaches, respectively. Although there are many supervised learning approaches like Naïve Bayes (NB), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF) and Artificial Neural Networks (ANN) to predict the emergence of the outbreaks that appeared before, it is also possible to detect any outbreak which are unprecedented before by using unsupervised learning approaches like principal component and cluster analysis. In this study, it is aimed to present a detailed analysis of machine learning approaches in outbreak detecting area to give a lead to the researchers who want to work in this area.

Anahtar Kelimeler

machine learning, outbreak early detection, pandemic, prediction

Kaynakça

• A. Şenol, H. Karacan, “A Survey on Data Stream Clustering Techniques”, European Journal of Science and Technology, 13, 17-30, 2018.
• S. Messaoud, et al., “A Survey on Machine Learning in Internet of Things: Algorithms, Strategies and Applications”, Internet of Things, 12, 2020.
• T. Meng, et al., “A Survey on Machine Learning for Data Fusion”, Information Fusion, 57, 115-129, 2020.
• C. Chen, “A Hybrid Intelligent Model of Analyzing Clinical Breast Cancer Data Using Clustering Techniques with Feature Selection”, Applied Soft Computing, 20, 4-14, 2014.
• J. Vamathevan, et al., “Applications of Machine Learning in Drug Discovery and Development”, Nature Reviews Drug Discovery, 18(6), 463-477, 2019.
• C. Réda, E. Kaufmann, A. Delahaye-Duriez, “Machine Learning Applications in Drug Development”, Computational and Structural Biotechnology Journal, 18, 241-252, 2020.
• M.T. Thai, et al., “Advanced Intelligent Systems for Surgical Robotics”, Advanced Intelligent Systems, 2(8), 2020.
• M. Jahanbani Fard, et al., “Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery”, Proceedings of the World Congress on Engineering and Computer Science, San Fransisco, October 19-21, 2016.
• A. Smiti, “When Machine Learning Meets Medical World: Current Status and Future Challenges”, Computer Science Review, 37, 2020.
• J. Friedman, T. Hastie, R. Tibshirani, “The Elements of Statistical Learning”, Springer Series in Statistics New York, 1, 2001.
• G. James, An Introduction to Statistical Learning, Springer, 2013.
• P. Cunningham, M. Cord, S.J. Delany, Supervised Learning, in Machine Learning Techniques for Multimedia, Springer, 21-49, 2008.
• T. Uyar, K. Karaca Uyar, Emre Yağlı, “Gözetimli Makine Öğrenmesiyle Noktalama ve Etkisiz Kelime Sıklıkları Kullanarak Yazar Tanıma”, Bilişim Teknolojileri Dergisi, 14(2), 183-190, 2021.
• A. Özgür, H. Erdem, “Saldırı Tespit Sistemlerinde Kullanılan Kolay Erişilen Makine Öğrenme Algoritmalarının Karşılaştırılması”, Bilişim Teknolojileri Dergisi, 5(2), 41-48, 2012.
• H.M. Abbas, M.M. Fahmy, “Neural Networks for Maximum Likelihood Clustering”, Signal Process, 36(1), 111-126, 1994.
• M.R. Ackermann, et al., “StreamKM++: A Clustering Algorithm for Data Streams”, Journal of Experimental Algorithmics, 17, 1-30, 2012.
• L.P. Kaelbling, M.L. Littman, A.W. Moore, “Reinforcement Learning: A Survey”, Journal of Artificial Intelligence Research, 4(1), 237–285, 1996.
• R. Nian, J. Liu, B. Huang, “A Review on Reinforcement Learning: Introduction and Applications in Industrial Process Control”, Computers & Chemical Engineering, 139, 2020.
• R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction, MIT Press, 2018.
• D. Grennan, “What Is a Pandemic?”, JAMA, 321(9), 910, 2019.
• G. R. Shinde, et al., “Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art”, SN Computer Science, 1(4), 197, 2020.
• M.S. Smolinski, A.W. Crawley, J.M. Olsen, “Finding Outbreaks Faster”, Health Security, 15(2), 215-220, 2017.
• T. Van-Dai, L. Chuan-Ming, G.W. Nkabinde, “Big data stream computing in healthcare real-time analytics”, IEEE International Conference on Cloud Computing and Big Data Analysis, 37-42, Chengdu, China, 2016.
• İnternet: Outbreak Investigation, https://www.who.int/hac/techguidance/training/outbreak%20investigation_en.pdf, 07.08.2020.
• İnternet: Toward the Development of Disease Early Warning Systems, https://www.ncbi.nlm.nih.gov/books/NBK222241/, 03.12.2020.
• A. Abdeslam, F. El Bouanani, H. Ben-azza, “Four Parallel Decoding Schemas of Product Block Codes”, Transactions on Networks and Communications, 2, 49-69, 2014.
• M. Ahmed, “Buffer-based Online Clustering for Evolving Data Stream”, Information Sciences, 489, 113-135, 2019.
• M. Hahsler, M. Bolaños, “Clustering Data Streams Based on Shared Density between Micro-Clusters”, IEEE Transactions on Knowledge and Data Engineering, 28(6), 1449-1461, 2016.
• N. Agarwal, et al., “Data Mining Techniques for Predicting Dengue Outbreak in Geospatial Domain Using Weather Parameters for New Delhi, India”, Current Science, 114, 2281-2291, 2018.
• S.A., Balamurugan, M.S.M. Mallick, Chinthana, “Improved prediction of dengue outbreak using combinatorial feature selector and classifier based on entropy weighted score based optimal ranking”, Informatics in Medicine Unlocked, 20, 2020.
• L. Tapak, et al., Comparative Evaluation of Time Series Models for Predicting Influenza Outbreaks: Application of Influenza-Like Illness Data from Sentinel Sites of Healthcare Centers in Iran. BMC Research Notes, 2019.
• İnternet: Malaria, https://www.who.int/news-room/fact-sheets/detail/malaria, 30.01.2021.
• V. Sharma, “Malaria Outbreak Prediction Model Using Machine Learning”, International Journal of Advanced Research in Computer Engineering & Technology, 9(3), 99-102, 2016.
• B. Modu, et al., “Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System”, Applied Sciences, 7(8), 2017.
• G. Comert, N. Begashaw, A. Turhan-Comert, “Malaria Outbreak Detection with Machine Learning Methods”, Biorxiv, 2020.
• İnternet: Dengue and severe dengue, https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue, 29.01.2021.
• G. Zhu, J. Hunter, Y. Jiang, “Improved Prediction of Dengue Outbreak Using the Delay Permutation Entropy”, IEEE International Conference on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber, Physical and Social Computing and IEEE Smart Data, 828-832, Chengdu, China, 2016.
• N. Iqbal, M. Islam, “Machine Learning for Dengue Outbreak Prediction: A Performance Evaluation of Different Prominent Classifiers”, Informatica, 43, 363-371, 2019.
• S. Anno, et al., “Spatiotemporal Dengue Fever Hotspots Associated with Climatic Factors in Taiwan Including Outbreak Predictions Based on Machine-Learning”, Geospatial Health, 14(2), 2019.
• R. Dhesi Baha, et al., “Artificial Intelligence Model as Predictor for Dengue Outbreaks”, Malaysian Journal of Public Health Medicine, 19(2), 2019.
• S. Amin, et al., “Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding”, IEEE Access, 8, 189054-189068, 2020.
• C.M. Benedum, et al., “Weekly Dengue Forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore”, PLOS Neglected Tropical Diseases, 14(10), 2020.
• A. Sadilek, et al., “Machine-Learned Epidemiology: Real-Time Detection of Foodborne Illness at Scale”, NPI Digital Medicine, 1, 2018.
• S. S. Chenar, Z. Deng, “Development of Artificial Intelligence Approach to Forecasting Oyster Norovirus Outbreaks Along Gulf of Mexico Coast”, Environment International, 111, 212-223, 2018.
• S. S. Chenar, Z. Deng, “Development of Genetic Programming-Based Model for Predicting Oyster Norovirus Outbreak Risks”, Water Research, 128, 20-37, 2018.
• F. S. Dawood, et al., “Estimated Global Mortality Associated with the First 12 Months of 2009 Pandemic Influenza a H1N1 Virus Circulation: A Modelling Study”, The Lancet infectious diseases, 12(9), 687-695, 2012.
• F. Koike, N. Morimoto, “Supervised Forecasting of the Range Expansion of Novel Non-Indigenous Organisms: Alien Pest Organisms and the 2009 H1N1 Flu Pandemic”, Global Ecology and Biogeography, 27(8), 991-1000, 2018.
• R. Liang, et al., “Prediction for Global African Swine Fever Outbreaks Based On A Combination of Random Forest Algorithms and Meteorological Data”, Transboundary and Emerging Diseases, 67, 935-946, 2019.
• S. Mezzatesta, et al., “A Machine Learning-Based Approach for Predicting the Outbreak of Cardiovascular Diseases in Patients on Dialysis”, Computer Methods and Programs in Biomedicine, 177, 9-15, 2019.
• A. A. Hemedan, et al., “Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach”, Scientific Reports, 10(1), 5058, 2020.
• S. Lim, C.S. Tucker, S. Kumara, “An Unsupervised Machine Learning Model For Discovering Latent Infectious Diseases Using Social Media Data”, Journal of Biomedical Informatics, 66, 82-94, 2017.
• D. Liu, “A Machine Learning Methodology for Real-Time Forecasting of the 2019-2020 COVID-19 Outbreak Using Internet Searches, News Alerts and Estimates From Mechanistic Models”, Arxiv, 1-23, 2020.
• J. Kumar, K. Hembram, “Epidemiological Study of Novel Coronavirus (COVID-19)”, Arxiv, 1-9, 2020.
• R. Fray da Silva, et al., “Unsupervised Machine Learning and Pandemics Spread: The Case of COVID-19”, SBCAS, 2020.
• S.F. Ardabili, et al., “COVID-19 Outbreak Prediction with Machine Learning”, Algorithms, 13(10), 1-36, 2020.
• S. Fong, et al., “Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak”, International Journal of Interactive Multimedia and Artificial Intelligence, 6, 132-140, 2020.
• Y. Karadayı, M.N. Aydin, A. Selçuk, “Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy”, IEEE Access, 8, 164155-164177, 2020.
• A. Khakharia, et al., “Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning”, Annals of Data Science, 8, 1-19, 2020.
• A. Behnam, R. Jahanmahin, “A data analytics approach for COVID-19 spread and end prediction (with a case study in Iran)”, Modelling Earth Systems and Environment, 1-11, 2021.
• V. La Gatta, et al., “An Epidemiological Neural network exploiting Dynamic Graph Structured Data Applied to the COVID-19 Outbreak”, IEEE Transactions on Big Data, 7(1), 45-55, 2020.
• D. Tiwari, et al., “Pandemic Coronavirus Disease (Covid‐19): World Effects Analysis And Prediction Using Machine‐Learning Techniques”, Expert Systems, 1-20, 2021.