Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran
Abstract Background and Aims Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID‐19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This stu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-04-01
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| Series: | Health Science Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/hsr2.1212 |
| _version_ | 1797839772891217920 |
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| author | Azita Yazdani Somayeh Kianian Bigdeli Maryam Zahmatkeshan |
| author_facet | Azita Yazdani Somayeh Kianian Bigdeli Maryam Zahmatkeshan |
| author_sort | Azita Yazdani |
| collection | DOAJ |
| description | Abstract Background and Aims Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID‐19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID‐19 by comparing the accuracy of machine learning (ML) models. Methods It is a cross‐sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K‐nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment. Results Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F‐score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death. Conclusion The development of software systems based on NB will be effective to predict the survival of COVID‐19 patients |
| first_indexed | 2024-04-09T16:03:31Z |
| format | Article |
| id | doaj.art-f49f3ac2ea8b4bf7a524cd1248060058 |
| institution | Directory Open Access Journal |
| issn | 2398-8835 |
| language | English |
| last_indexed | 2024-04-09T16:03:31Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Science Reports |
| spelling | doaj.art-f49f3ac2ea8b4bf7a524cd12480600582023-04-25T08:04:33ZengWileyHealth Science Reports2398-88352023-04-0164n/an/a10.1002/hsr2.1212Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of IranAzita Yazdani0Somayeh Kianian Bigdeli1Maryam Zahmatkeshan2Department of Health Information Management, School of Health Management and Information Sciences Shiraz University of Medical Sciences Shiraz IranHealth Information Management Department, School of Allied Medical Sciences Tehran University of Medical Sciences Tehran IranNoncommunicable Diseases Research Center Fasa University of Medical Sciences Fasa IranAbstract Background and Aims Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID‐19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID‐19 by comparing the accuracy of machine learning (ML) models. Methods It is a cross‐sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K‐nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment. Results Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F‐score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death. Conclusion The development of software systems based on NB will be effective to predict the survival of COVID‐19 patientshttps://doi.org/10.1002/hsr2.1212COVID‐19decision treeK‐nearest neighborsmachine learningNaive Bayesrandom forest |
| spellingShingle | Azita Yazdani Somayeh Kianian Bigdeli Maryam Zahmatkeshan Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran Health Science Reports COVID‐19 decision tree K‐nearest neighbors machine learning Naive Bayes random forest |
| title | Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran |
| title_full | Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran |
| title_fullStr | Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran |
| title_full_unstemmed | Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran |
| title_short | Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran |
| title_sort | investigating the performance of machine learning algorithms in predicting the survival of covid 19 patients a cross section study of iran |
| topic | COVID‐19 decision tree K‐nearest neighbors machine learning Naive Bayes random forest |
| url | https://doi.org/10.1002/hsr2.1212 |
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