Nutrition & Santé
Volume 11, Numéro 1, Pages 33-47

An Artificial Neural Networks Model For Predicting Patients’ Mortality Due To Covid-19 "covisurv2021"

Authors : Haddad Fatima Zohra . Ouadah Saliha . Lefilef Lina . Benbouras Mohammed Amin .


Introduction. COVID-19 is, surely, the pandemic of the century with the unusual circumstances it generated. Subsequently, there has been medical and human scarcity of resources leading to the health system collapse, especially in third world countries. Objective. To support the white army in grasping the pandemic behavior, several studies have pointed to the existence of patient-related factors affecting COVID-19 patients’ mortality- risk. In the current study, Artificial Neural Network has been employed to predict COVID-19 mortality. Material and methods. In particular, the modeling phase was done using a database of 684 samples collected from Mohamed Seddik Ben Yahia hospital, Jijel. This latter contains the antecedent disease with blood biomarker data of the patients. Firstly, 18 parameters were selected in the input layer based on the literature recommendation and expert medical team consultation. Furthermore, the optimal inputs have been modeled using Artificial Neural Network and their performance was assessed through four performance measures (sensitivity, specificity, precision, and accuracy). Results. The comparative study proved the effectiveness of (18-12-2) model trained by Tansig transfer function, which displayed a higher performance in predicting COVID-19 mortality compared to other models proposed in the literature. Afterward, the proposed optimal model was utilized to develop a GUI public interface by Matlab software. Conclusion. Finally, a reliable and easy-to-use graphical interface is generated in the current study dubbed “CoviSurv2021”. This latter will be very helpful for the medical staff to select priority patients who have upper urgency to be hospitalized, prioritize patients when the hospital is overcrowded, and gain time to provide the care needed.


Covid-19 ; SARS-Cov-2 ; Corona Virus ; Artificial Neural Networks ; CoviSurv2021