Prediction of CoVid-19 mortality in Iraq-Kurdistan by using Machine learning


  • Brzu T. Muhammed Department of Computer Science, Kurdistan Technical Institute, Sulaymaniyah, Iraq
  • Ardalan Husin Awlla Department Information Technology, University of Human Development, Sulaymaniyah
  • Sherko H. Murad Department of Computer Science, Kurdistan Technical Institute, Sulaymaniyah, Iraq
  • Sabah N. Ahmad Department of Computer Science, Kurdistan Technical Institute, Sulaymaniyah, Iraq



Coronavirus disease, Coronavirus, Forecasting, Machine learning, Kurdistan-IRAQ


This research analyzed different aspects of coronavirus disease (COVID-19) for patients who have coronavirus, for find out which aspects have an effect to patient death. First, a literature has been made with the previous research that has been done on the analysis dataset of coronavirus using Machine learning (ML) algorithm. Second, data analytics is applied on a dataset of Sulaymaniyah, Iraq, to find factors that affect the mortality rate of coronavirus patients. Third, classification algorithms are used on a dataset of 1365 samples provided by hospitals in Sulaymaniyah, Iraq to diagnose COVID-19. Using ML algorithm provided us to find mortality rate of this disease, and detect which factor has major effect to patient death. It is shown here that support vector machine (SVM), decision tree (DT), and naive Bayes algorithms can classify COVID-19 patients, and DT is best one among them at an accuracy (96.7 %).


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