The impact of preprocessing techniques for Covid-19 mortality prediction

Abstract

Coronavirus 2019 (COVID-19), as a common infectious disease, is spreading rapidly and uncontrollably worldwide. Therefore, early detection of mortality considering the symptoms that appear in patients with Coronavirus is important. The main aim of this study is investigating the effect of data preprocessing methods on the efficiency of data mining approaches. In this study, we propose a hybrid method based on the Covid-19 dataset to predict the mortality of 1255 patients with coronavirus that has three main steps. In the first step, preprocessing methods such as imputing missing values, data balancing, normalization, and filter-based feature selection are used on raw data. Then the classification algorithms are applied to the data and finally, the evaluation is done. The results of the proposed method show its effectiveness in predicting mortality from coronavirus disease. Therefore, doctors and treatment staff can use this model to early diagnose of factors affecting the mortality of patients and with timely treatment, the mortality rate due to Covid-19 is reduced.

Keywords

• covid-19
• artificial intelligence
• data mining
• feature selection
• knime tool