Comorbidity Based Risk Prediction System for ARDS in COVID-19 Patients
10th International Conference on Advances in Computing and Communications, ICACC 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1741183
ABSTRACT
The Coronavirus disease is an acute respiratory disease that has been designated as a pandemic by the WHO(World Health Organization).The rapid increase in the number of illnesses and death rates has put enormous strain on public health services. Hence, its critical to recognize the comorbidities in COVID-19 patients that led to ARDS(Acute Respiratory Distress Syndrome). In this paper, we use machine learning and deep learning methods to classify high risk COVID-19 patients with accurate results. This paper might speed up decisions made in public health services for predicting medical resources as well as early classification of high risk COVID-19 patients. © 2021 IEEE.
ADABOOST; ARDS (Acute Respiratory Distress Syndrome); Artificial Neural Network; COVID-19; Gradient Boosted Trees; KNN; Random Forest; Support Vector Machine; XGBOOST; Adaptive boosting; Convolutional neural networks; Decision trees; Deep learning; Health risks; Public health; Acute respiratory distress syndrome; Comorbidities; Gradient boosted tree; Public health services; Random forests; Support vectors machine; Support vector machines
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
10th International Conference on Advances in Computing and Communications, ICACC 2021
Year:
2021
Document Type:
Article
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