SARS-CoV-2 Mortality Risk Prediction Using Machine Learning Algorithms to Aid Medical Decision-Making
4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022
; 563:293-306, 2023.
Article
in English
| Scopus | ID: covidwho-2280646
ABSTRACT
The coronavirus has affected the world in every possible aspect such as loss of economy, infrastructure, and moreover human life. In the era of growing technology, artificial intelligence and machine learning can help find a way in reducing mortality so, we have developed a model which predict the mortality risk in patients infected by COVID-19. We used the dataset of 146 countries which consists of laboratory samples of COVID-19 cases. This study presents a model which will assist hospitals in determining who must be given priority for treatment when the system is overburdened. As a result, the accuracy of the mortality rate prediction demonstrated is 91.26%. We evaluated machine learning algorithms namely decision tree, support vector machine, random forest, logistic regression, and K-nearest neighbor for prediction. In this study, the most relevant features and alarming symptoms were identified. To evaluate the results, different performance measures were used on the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022
Year:
2023
Document Type:
Article
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