Smart COVID-19 Prediction System Using Neural Network


  • Ala Saeed Khudhur Computer Science and IT Department, College of Science, Salahaddin University-Erbil, Kurdistan Region, Iraq
  • Abdulqadir Ismail Abdullah Computer Science Department, College of Science, International University of Erbil-Erbil, Kurdistan Region, Iraq



COVID-19; Artificial Neural Network; FFBNN; PNN‎


The pandemic of coronavirus COVID-19 has created a great danger and concern for humanity. Many researchers have done different types of work in this area to provide medical services. In this paper, we proposed a smart Covid-19 diagnosis system by using a Feed Forward Backpropagation Neural Network (FFBNN) and Probabilistic Neural Network (PNN). Based on personal information from patients such as (age, gender, contact with sick person) and five symptoms (headache, fever, cough, sore throat, and shortness of breath) for this purpose we used 510 samples that are collected from different sources, and then compared to previous studies. Results of this work showed that using FFBNN has achieved highest accuracy (98.0%), sensitivity (100%), specificity (94.4%), precision (97.1%), recall (100%) and F1-score (98.52%). But PNN that has accuracy, sensitivity, specificity, precision, recall, F1-score of 90.2%, 92.7%, 87.2%, 89.47%, 92.7% and 91.07% respectively. The most relevant features to positive Covid-19 were fever, shortness of breath, and cough with correlation coefficient of 0.591, 0.495 and 0.488.


AHMADLOU, M. and ADELI, H. 2010. Enhanced probabilistic neural network with local decision circles: A robust classifier. Integrated Computer-Aided Engineering. 17(3), pp.197–210.

ANSARI, S., SHAFI, I., ANSARI, A., AHMAD, J. and SHAH, S.I. 2011. Diagnosis of liver disease induced by hepatitis virus using artificial neural networks In: 2011 IEEE 14th international multitopic conference. IEEE, pp.8–12.

BEBIS, G. and GEORGIOPOULOS, M. 1994. Feed-forward neural networks. IEEE Potentials. 13(4), pp.27–31.

BHARDWAJ, A. and TIWARI, A. 2015. Breast cancer diagnosis using genetically optimized neural network model. Expert Systems with Applications. 42(10), pp.4611–4620.

El_JERJAWI, N.S. and ABU-NASER, S.S. 2018. Diabetes prediction using artificial neural network. International Journal of Advanced Science and Technology. 121.

GEORGE, D.N., JEHLOL, H.B. and OLEIWI, A.S.A. 2015. Brain tumor detection using shape features and machine learning algorithms. International Journal of Advanced Research in Computer Science and Software Engineering. 5(10), pp.454–459.

KHOZEIMEH, F., SHARIFRAZI, D., IZADI, N.H., JOLOUDARI, J.H., SHOEIBI, A., ALIZADEHSANI, R., GORRIZ, J.M., HUSSAIN, S., SANI, Z.A. and MOOSAEI, H. 2021. Combining a convKhozeimeh, F., Sharifrazi, D., Izadi, N.H., Joloudari, J.H., Shoeibi, A., Alizadehsani, R., Gorriz, J.M., Hussain, S., Sani, Z.A. and Moosaei, H. 2021. Combining a convolutional neural network with autoencoders to predict the survival chan. Scientific Reports. 11(1), pp.1–18.

KRISHNN, S., MAGALINGAM, P. and IBRAHIM, R. 2021. Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction. International Journal of Electrical & Computer Engineering (2088-8708). 11(6).

LU, H., STRATTON, C.W. and TANG, Y. 2020. Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle. Journal of medical virology. 92(4), p.401.

MENNI, C., VALDES, A.M., FREIDIN, M.B., SUDRE, C.H., NGUYEN, L.H., DREW, D.A., GANESH, S., VARSAVSKY, T., CARDOSO, M.J. and MOUSTAFA, J.S.E.-S. 2020. Real-time tracking of self-reported symptoms to predict potential COVID-19. Nature medicine. 26(7), pp.1037–1040.

PATTERSON, B.K., GUEVARA-COTO, J., YOGENDRA, R., FRANCISCO, E.B., LONG, E., PISE, A., RODRIGUES, H., PARIKH, P., MORA, J. and MORA-RODRÍGUEZ, R.A. 2021. Immune-based prediction of COVID-19 severity and chronicity decoded using machine learning. Frontiers in immunology. 12, p.2520.

PRAKASH, K.B., IMAMBI, S.S., ISMAIL, M., KUMAR, T.P. and PAWAN, Y.N. 2020. Analysis, prediction and evaluation of covid-19 datasets using machine learning algorithms. International Journal. 8(5).

RUFAI, A.U.S. and UMAR, M. 2018. Using artificial neural networks to diagnose heart disease In: IJCAI., pp.1–6.

SARITAS, M.M. and YASAR, A. 2019. Performance analysis of ANN and Naive Bayes classification algorithm for data classification. International Journal of Intelligent Systems and Applications in Engineering. 7(2), pp.88–91.

SUDRE, C.H., LEE, K.A., LOCHLAINN, M.N., VARSAVSKY, T., MURRAY, B., GRAHAM, M.S., MENNI, C., MODAT, M., BOWYER, R.C.E. and NGUYEN, L.H. 2021. Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app. Science Advances. 7(12), p.eabd4177.

TOSTMANN, A., BRADLEY, J., BOUSEMA, T., YIEK, W.-K., HOLWERDA, M., BLEEKER-ROVERS, C., TEN OEVER, J., MEIJER, C., RAHAMAT-LANGENDOEN, J. and HOPMAN, J. 2020. Strong associations and moderate predictive value of early symptoms for SARS-CoV-2 test positivity among healthcare workers, the Netherlands, March 2020. Eurosurveillance. 25(16), p.2000508.

TULI, SHRESHTH, TULI, SHIKHAR, TULI, R. and GILL, S.S. 2020. Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things. 11, p.100222.

VIJAYARANI, S., DHAYANAND, S. and PHIL, M. 2015. Kidney disease prediction using SVM and ANN algorithms. International Journal of Computing and Business Research (IJCBR). 6(2), pp.1–12.

WU, J., ZHANG, P., ZHANG, L., MENG, W., LI, J., TONG, C., LI, Y., CAI, J., YANG, Z. and ZHU, J. 2020. Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results. MedRxiv.

WU, W., WANG, A. and LIU, M. 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 395(10223), pp.497–506.

ZENS, M., BRAMMERTZ, A., HERPICH, J., SÜDKAMP, N. and HINTERSEER, M. 2020. App-based tracking of self-reported COVID-19 symptoms: analysis of questionnaire data. Journal of medical Internet research. 22(9), p.e21956.

ZHOU, K., SUN, Y., LI, L., ZANG, Z., WANG, J., LI, J., LIANG, J., ZHANG, F., ZHANG, Q. and GE, W. 2020. Eleven Routine Clinical Features Predict COVID-19 Severity. medRxiv.

ZOABI, Y., DERI-ROZOV, S. and SHOMRON, N. 2021. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. npj digital medicine. 4(1), pp.1–5.





Mathematics ,Physics and Engineering Researches