Symptom Based COVID-19 Prediction Using Machine Learning and Deep Learning Algorithms

Yıl 2022, Cilt: 2 Sayı: 1, 22 - 29, 19.07.2022

Nesibe Yalçın Sibel Ünaldı

Öz

Research studies are carried out in many areas of science to cope with the impacts of the COVID-19 crisis in the world. Machine learning can be used for purposes such as understanding, addressing, fighting, and preventing - controlling COVID-19. In this research, the presence of COVID-19 has been predicted using K Nearest Neighbor, Support Vector Machines, Logistic Regression, and Multilayer Perceptual Neural Networks machine learning and Gated Recurrent Unit (GRU) and Long Short-Term Memory deep learning algorithms. A publicly available dataset that includes various features (i.e. wearing masks, abroad travel, contact with the COVID patient) and symptoms (i.e. breathing problems, fever, and dry cough) is used for the COVID-19 diagnosis prediction. The learning algorithms have been compared according to the evaluation metrics. The experimental results have been shown that GRU deep learning algorithm is more reliable with a prediction accuracy of 98.65% and a loss/mean squared error of 0.0126.

Anahtar Kelimeler

COVID-19, deep learning, symptom, machine learning, prediction

Kaynakça

• N.Alballa and I. Al-Turaiki, “Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review”, Inform. Med. Unlocked, vol. 24, pp. 100564 (1-17), 2021.
• C. I.Paules,H. D. Marston, and A. S.Fauci, “Coronavirus infections - more than just the common cold”, JAMA: J. Am. Med. Assoc., vol. 323, pp. 707-708, 2020.
• WHO, “Virtual press conference on COVID-19 - 11 March 2020”, 25 January 2022, Available online: https://www.who.int/docs/default-source/coronaviruse/transcripts/who -audio-emergencies-coronavirus-press-conference-full-and-final-11mar2020.pdf, 2020.
• Y.Zoabi, S.Deri-Rozov, andN.Shomron, “Machine learning-based prediction of COVID-19 diagnosis based on symptoms”, NPJ Digit. Med., vol. 4, pp. 3 (1-5), 2021.
• WHO, “WHO coronavirus disease (COVID-19) dashboard”, 13 January 2022, Available online: https://covid19.who.int/, 2022.
• F. Wu et al., “A new coronavirus associated with human respiratory disease in China”, Nature, vol. 579 (7798), pp. 265-269, 2020.
• O. Sevli andV. G.Başer, “COVID-19 salgınına yönelik zaman serisi verileri ile Prophet model kullanarak makine öğrenmesi temelli vaka tahminlemesi”, European Journal of Science and Technology, vol. 19, pp. 827-835, 2020.
• A. S. Kwekha-Rashid, H. N. Abduljabbar, andB. Alhayani, “Coronavirus disease (COVID-19) cases analysis using machine-learning applications”, Appl. Nanosci., pp. 1-13, 2021.
• M.Naseem, R.Akhund, H.Arshad, andM. T. Ibrahim, “Exploring the potential of artificial intelligence and machine learning to combat COVID-19 and existing opportunities for LMIC: a Scoping review”, J. Prim. Care Community Health, vol. 11, pp. 1-11, 2020.
• Jamshidi M. et al., “Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment”, IEEE Access, vol. 8, pp. 109581–109595, 2020.
• E.Dinçmen, “Makine öğrenmesi ve Covid-19”, 20 January 2022, Available online: https://www.isikun.edu.tr/web/1695-15661-1-1/isik_universitesi/hakkinda/yonetim__ idari_birimler__kurumsal_iletisim_daire_baskanligi__basinda_isik_universitesi__isik_yazilari/makine_ogrenmesi_ve_covid-19 (2022)
• Z. A. A. Alyasseri et al., “Review on COVID-19 diagnosis models based on machine learning and deep learning approaches”, Expert Syst., pp. e12759 (1-32), 2021.
• S. B.Rikan, A. S.Azar, A.Ghafari, J. B.Mohasefi, and H.Pirnejad, “COVID-19 diagnosis from routine blood tests using artificial intelligence techniques”, Biomed. Signal Process. Control, vol. 72,pp. 103263 (1-16), 2022.
• Y. Lee et al., “The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection”, Sci. Rep., vol. 12, pp. 1234 (1-10), 2022.
• D.Yang, C.Martinez, L.Visuña, H.Khandhar, C.Bhatt, and J.Carretero, “Detection and analysis of COVID-19 in medical images using deep learning techniques”, Sci. Rep., vol. 11, pp. 19638 (1-13), 2021.
• F.Zhang, “Application of machine learning in CT images and X-rays of COVID-19 pneumonia”, Medicine, vol. 100 (36), pp. e26855 (1-13), 2021.
• S.Guhathakurata, S.Kundu, A.Chakraborty, and J. S.Banerjee, “A novel approach to predict COVID-19 using support vector machine”, Data Science for COVID-19, pp. 351-364, 2021.
• N. S.Özen, S.Saraç, and M.Koyuncu, “COVID-19 vakalarının makine öğrenmesi algoritmaları ile tahmini: Amerika Birleşik Devletleri örneği”, European Journal of Science and Technology, vol. 22,pp. 134-139, 2021.
• R. Ünlü and E.Namlı, “Machine learning and classical forecasting methods based decision support systems for COVID-19”, Comput., Mater. Contin., vol. 64(3), pp. 1383-1399, 2020.
• C. N.Villavicencio, J. J. E.Macrohon, X. AInbaraj., J. -H.Jeng, and J. -G.Hsieh, “COVID-19 prediction applying supervised machine learning algorithms with comparative analysis using WEKA”, Algorithms, vol. 14(7), pp. 201 (1-22), 2021.
• M. Malik et al., “Determination of COVID-19 patients using machine learning algorithms”, Intell. Autom. Soft Comput., vol. 31(1), pp. 207-222, 2022.
• F.Shahid, A.Zameer, and M.Muneeb, “Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM”, Chaos Solit. Fractals, vol. 140, pp. 110212 (1-9), 2020.
• T. B.Alakus and I.Turkoglu, “Comparison of deep learning approaches to predict COVID-19 infection”, Chaos Solit. Fractals, vol. 140, pp. 110120 (1-7), 2020.
• K.Moulaei, M.Shanbehzadeh, Z.Mohammadi-Taghiabad, and H.Kazemi-Arpanahi, “Comparing machine learning algorithms for predicting COVID-19 mortality”, BMC Medical Inform. Decis. Mak., vol. 22, pp. 2(1-12), 2022.
• A. B.Majumder, S.Gupta, D.Singh, and S.Majumder, “An intelligent system for prediction of COVID-19 case using machine learning framework-logistic regression”, J. Phys. Conf. Ser., vol. 1797 (1),pp. 012011(1-9), 2021.
• J. M. Antoñanzas et al. “Symptom-Based Predictive Model of COVID-19 Disease in Children”, Viruses, vol. 14, pp. 63, 2022.
• C. Huang et al., “Clinical features of patients infected with 2019 novel Coronavirus in Wuhan, China”, The Lancet, vol. 395(10223), pp. 497-506, 2020.
• N. Chen et al., “Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study”, The Lancet, vol. 395(10223),pp. 507-513, 2020.
• Kaggle, “Symptoms and COVID Presence”, 8 January 2022. Available online: https://www.kaggle.com/hemanthhari/symptoms-and-covid-presence
• A. Tsatsakis et al., “SARS-CoV-2 pathophysiology and its clinical implications: An integrative overview of the pharmacotherapeutic management of COVID-19”, Food Chem. Toxicol., vol. 146,pp. 111769, 2020.
• E. Karaahmetoğlu, S.Ersöz, A. K.Türker, V.Ateş, and A. F.İnal, “Evaluation of profession predictions for today and the future with machine learning methods: emperical evidence from Turkey”, Journal of Polytechnic, (in press)
• I.Balikci Cicek and Z.Kucukakcali, “Classification of prostate cancer and determination of related factors with different artificial neural network”, Middle Black Sea Journal of Health Science, vol. 6(3), pp. 325-332, 2020.
• N.Yalcin, G.Tezel, and C.Karakuzu, “Epilepsy diagnosis using artificial neural network learned by PSO”, Turk. J. Electr. Eng. Comput. Sci., vol. 23(2), pp. 421-432, 2015.
• S.Cakir, S.Toklu, and N.Yalcin, “RPL attack detection and prevention in the Internet of Things networks using a GRU based deep learning”, IEEE Access, vol. 8, pp. 183678-183689, 2020.