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A Classification Process Using Decision Tree Machine Learning Model for Determining the Effect of Anticoagulants on the Fate of Different Types of COVID-19 Patients with Other Diseases

Authors: Baraa Alnassif; Sira Astour;

A Classification Process Using Decision Tree Machine Learning Model for Determining the Effect of Anticoagulants on the Fate of Different Types of COVID-19 Patients with Other Diseases

Abstract

BACKGROUND The high rate of Covid-19 deaths with thromboembolism has become a global concern, the disease is further complicated by the state of hypercoagulability, as there are many studies that indicate an increase in mortality rates. As a result, anticoagulants were used for COVID-19 patients generally as a treatment to prevent thrombosis. OBJECTIVE Knowing the extent of the impact of a disease or a group of diseases using anticoagulants on the lives of Corona patients METHODS A retrospective analytical study was conducted on the data of 1131 COVID-19 patients who were recorded from the date 30/8/2020 to 11/5/2021 in detail, including personal data, clinical symptoms, medical history, treatment, and the fate of each patient. The Decision Tree (DT) algorithm was applied to a specific part of the analyzed patient data, which are the age, oxygenation, heart diseases, neurological, vascular, pulmonary diseases, pressure, and renal failure, in addition to the anticoagulants, mechanical ventilation in the medicine prescription, to be classified into two groups, either death or recovery RESULTS Using Enoxaparin and Rivaroxaban together did not produce positive results for patients with renal failure and elderly patients over 64 years whose decreased oxygenation was less than 90%. In the event that Enoxaparin uses for patients whose decreased oxygenation (76% or less) and who suffered from pulmonary fibrosis was beneficial, and for those whose other pulmonary diseases and healthy people, it was not beneficial. Using Enoxaparin for patients whose oxygenation was more than 76% and who had pulmonary fibrosis was bad, but with the rest of the patients, it was beneficial. CONCLUSIONS The use of anticoagulants improves the chance of survival for COVID-19 patients, especially low partial weight heparin, and it is very necessary before prescribing any preventive or therapeutic anticoagulant to take the patient’s condition into consideration, especially for patients with renal or pulmonary insufficiency or those suffering from a decrease in oxygenation, so that we need special recommendations for each case of patients.

Keywords

anticoagulants, classification, covid-19, Science, decision tree, Q, clinical data

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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