Comparing Supervised Machine Learning Models for Covid-19 patient detection using a Combination of Clinical and Laboratory Dataset
COVID-19 is a new variant of SARS-COV-2 which can lead to mild to severe infection in humans. Despite the remarkable efforts to contain the epidemic, the virus spread rapidly around the world and its prevalence continued with different degrees of clinical symptoms in many countries. Although common...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of science and culture
2022-01-01
|
| Series: | International Journal of Web Research |
| Subjects: | |
| Online Access: | https://ijwr.usc.ac.ir/article_154303_37f199ff79a212651110f3d1718cc148.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559949459587072 |
|---|---|
| author | Narges Mohebbi Mehdi Tutunchian Meysam Alavi Mehrdad Kargari Amir Behnam Kharazmy |
| author_facet | Narges Mohebbi Mehdi Tutunchian Meysam Alavi Mehrdad Kargari Amir Behnam Kharazmy |
| author_sort | Narges Mohebbi |
| collection | DOAJ |
| description | COVID-19 is a new variant of SARS-COV-2 which can lead to mild to severe infection in humans. Despite the remarkable efforts to contain the epidemic, the virus spread rapidly around the world and its prevalence continued with different degrees of clinical symptoms in many countries. Although common strategies including prevention, diagnosis, and care are necessary to curb this epidemic, early and accurate diagnosis can play an important role in reducing the speed of the epidemic. In this regard, the use of technologies based on artificial intelligence can be of great help. For this reason, since the outbreak of COVID-19, many researchers have tried to use machine learning techniques as a subset of artificial intelligence for the early diagnosis of COVID-19. Considering the importance and role of using clinical and laboratory data in the diagnosis of people with covid-19, in this paper K-NN, SVM, decision tree, random forest, Naive Bayes, neural network and XGBoost models are the most common machine learning models, and a dataset containing 1354 records consisting of clinical and laboratory data of patients in Imam Hossein Hospital in Tehran has been used to diagnose patients with covid-19. The results of this research indicate that based on the evaluation criteria, XGBoost and K-NN models have the most accuracy among the mentioned models and can be considered suitable predictive models for the diagnosis of COVID-19. |
| format | Article |
| id | doaj-art-84c5f8880ab14f5099ae96430ec898aa |
| institution | Kabale University |
| issn | 2645-4343 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | University of science and culture |
| record_format | Article |
| series | International Journal of Web Research |
| spelling | doaj-art-84c5f8880ab14f5099ae96430ec898aa2025-01-05T11:34:14ZengUniversity of science and cultureInternational Journal of Web Research2645-43432022-01-015181810.22133/ijwr.2022.348799.1127Comparing Supervised Machine Learning Models for Covid-19 patient detection using a Combination of Clinical and Laboratory DatasetNarges Mohebbi0Mehdi Tutunchian1Meysam Alavi 2https://orcid.org/0009-0009-9212-2809Mehrdad Kargari 3Amir Behnam Kharazmy 4Department of Information Technology, Tarbiat Modares University, Tehran, Iran Department of Information Technology, Tarbiat Modares University, Tehran, IranDepartment of Information Technology, Tarbiat Modares University, Tehran, IranDepartment of Information Technology, Tarbiat Modares University, Tehran, IranDepartment of Internal Medicine, School of Medicine, Shahid Beheshti University of Medical Sciences, Imam Hossein Hospital, Tehran, IranCOVID-19 is a new variant of SARS-COV-2 which can lead to mild to severe infection in humans. Despite the remarkable efforts to contain the epidemic, the virus spread rapidly around the world and its prevalence continued with different degrees of clinical symptoms in many countries. Although common strategies including prevention, diagnosis, and care are necessary to curb this epidemic, early and accurate diagnosis can play an important role in reducing the speed of the epidemic. In this regard, the use of technologies based on artificial intelligence can be of great help. For this reason, since the outbreak of COVID-19, many researchers have tried to use machine learning techniques as a subset of artificial intelligence for the early diagnosis of COVID-19. Considering the importance and role of using clinical and laboratory data in the diagnosis of people with covid-19, in this paper K-NN, SVM, decision tree, random forest, Naive Bayes, neural network and XGBoost models are the most common machine learning models, and a dataset containing 1354 records consisting of clinical and laboratory data of patients in Imam Hossein Hospital in Tehran has been used to diagnose patients with covid-19. The results of this research indicate that based on the evaluation criteria, XGBoost and K-NN models have the most accuracy among the mentioned models and can be considered suitable predictive models for the diagnosis of COVID-19.https://ijwr.usc.ac.ir/article_154303_37f199ff79a212651110f3d1718cc148.pdfcovid-19coronavirusearly detectionmachine learning techniquessupervised model |
| spellingShingle | Narges Mohebbi Mehdi Tutunchian Meysam Alavi Mehrdad Kargari Amir Behnam Kharazmy Comparing Supervised Machine Learning Models for Covid-19 patient detection using a Combination of Clinical and Laboratory Dataset International Journal of Web Research covid-19 coronavirus early detection machine learning techniques supervised model |
| title | Comparing Supervised Machine Learning Models for Covid-19 patient detection using a Combination of Clinical and Laboratory Dataset |
| title_full | Comparing Supervised Machine Learning Models for Covid-19 patient detection using a Combination of Clinical and Laboratory Dataset |
| title_fullStr | Comparing Supervised Machine Learning Models for Covid-19 patient detection using a Combination of Clinical and Laboratory Dataset |
| title_full_unstemmed | Comparing Supervised Machine Learning Models for Covid-19 patient detection using a Combination of Clinical and Laboratory Dataset |
| title_short | Comparing Supervised Machine Learning Models for Covid-19 patient detection using a Combination of Clinical and Laboratory Dataset |
| title_sort | comparing supervised machine learning models for covid 19 patient detection using a combination of clinical and laboratory dataset |
| topic | covid-19 coronavirus early detection machine learning techniques supervised model |
| url | https://ijwr.usc.ac.ir/article_154303_37f199ff79a212651110f3d1718cc148.pdf |
| work_keys_str_mv | AT nargesmohebbi comparingsupervisedmachinelearningmodelsforcovid19patientdetectionusingacombinationofclinicalandlaboratorydataset AT mehditutunchian comparingsupervisedmachinelearningmodelsforcovid19patientdetectionusingacombinationofclinicalandlaboratorydataset AT meysamalavi comparingsupervisedmachinelearningmodelsforcovid19patientdetectionusingacombinationofclinicalandlaboratorydataset AT mehrdadkargari comparingsupervisedmachinelearningmodelsforcovid19patientdetectionusingacombinationofclinicalandlaboratorydataset AT amirbehnamkharazmy comparingsupervisedmachinelearningmodelsforcovid19patientdetectionusingacombinationofclinicalandlaboratorydataset |