Predicting-high-risk-of-death-patients-due-to-COVID-19-using-classification-algorithms

Knowing who is going to be a patient at high risk of death is crucial in designing the right treatment for each individual. Implementing classification algorithms into COVID- 19 disease data, we can determine whether a person is at high risk or not, which express all problems of the patient, since changes necessary will be applied to improve the quality of healthcare. This study focused on the machine learning (Naive Bayes, Knearest neighbors and Decision Tree) on the COVID-19 dataset provided by the Mexican government. The final results were compared and evaluated to find the most effective model based on different evaluation criteria. The final results were compared and evaluated to find the most effective model based on different evaluation criteria. The experimental results show that K-nearest neighbors algorithm outperformed than two other algorithms in terms of the accuracy, precision and F1-score measurement, with 91.27%, 88.21% and 91.58% on the death classification task. However, Decision Tree has the highest recall with 95.25%. With the use of this application, the hospital can propose an appropriate method to improve the quality of healthcare in the future.