Early stage prediction of COVID-19 Using machine learning model
The healthcare sector has traditionally been an early use of technological progress and has achieved significant advantages, especially in the field of machine learning like the prediction of diseases. The COVID-19 epidemic is still having an impact on every facet of life and necessitates a fast an...
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| Format: | Article |
| Language: | English |
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College of Computer and Information Technology – University of Wasit, Iraq
2023-03-01
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| Series: | Wasit Journal of Computer and Mathematics Science |
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| Online Access: | https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/107 |
| _version_ | 1797198919258603520 |
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| author | mohammed Al-Hasnawi Abdulkareem Radhi |
| author_facet | mohammed Al-Hasnawi Abdulkareem Radhi |
| author_sort | mohammed Al-Hasnawi |
| collection | DOAJ |
| description |
The healthcare sector has traditionally been an early use of technological progress and has achieved significant advantages, especially in the field of machine learning like the prediction of diseases. The COVID-19 epidemic is still having an impact on every facet of life and necessitates a fast and accurate diagnosis. Early detection of COVID-19 is exceptionally critical to saving the lives of human beings. The need for an effective, rapid, and precise way to reduce consultants' workload in diagnosing suspected cases has emerged. This paper presents a proposed model that aims to design and implement an automated model to predict COVID-19 with high accuracy in the early stages. The dataset used in this study considers an imbalanced dataset and converted to a balanced one using Synthetic Minority Over Sampling Technique (SMOTE). Filter-based feature selection method and many machine learning algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Logistic Regression, and Random Forest (RF) is used in this model. Since the best classification result was achieved by using the RF algorithm, and this algorithm was optimized by tuning the hyperparameters. The optimized RF enhanced the accuracy from 98.0 to 99.5.
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| first_indexed | 2024-03-07T19:03:19Z |
| format | Article |
| id | doaj.art-5680af9d24f142f1985a40c5183fe3b0 |
| institution | Directory Open Access Journal |
| issn | 2788-5879 2788-5887 |
| language | English |
| last_indexed | 2024-04-24T07:07:30Z |
| publishDate | 2023-03-01 |
| publisher | College of Computer and Information Technology – University of Wasit, Iraq |
| record_format | Article |
| series | Wasit Journal of Computer and Mathematics Science |
| spelling | doaj.art-5680af9d24f142f1985a40c5183fe3b02024-04-21T18:56:36ZengCollege of Computer and Information Technology – University of Wasit, IraqWasit Journal of Computer and Mathematics Science2788-58792788-58872023-03-012110.31185/wjcm.107Early stage prediction of COVID-19 Using machine learning model mohammed Al-Hasnawi0Abdulkareem Radhi1Computer Department/College of Science University AL-Nahrain, Baghdad, 10001, IraqComputer Department/College of Science University AL-Nahrain, Baghdad, 10001, Iraq The healthcare sector has traditionally been an early use of technological progress and has achieved significant advantages, especially in the field of machine learning like the prediction of diseases. The COVID-19 epidemic is still having an impact on every facet of life and necessitates a fast and accurate diagnosis. Early detection of COVID-19 is exceptionally critical to saving the lives of human beings. The need for an effective, rapid, and precise way to reduce consultants' workload in diagnosing suspected cases has emerged. This paper presents a proposed model that aims to design and implement an automated model to predict COVID-19 with high accuracy in the early stages. The dataset used in this study considers an imbalanced dataset and converted to a balanced one using Synthetic Minority Over Sampling Technique (SMOTE). Filter-based feature selection method and many machine learning algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Logistic Regression, and Random Forest (RF) is used in this model. Since the best classification result was achieved by using the RF algorithm, and this algorithm was optimized by tuning the hyperparameters. The optimized RF enhanced the accuracy from 98.0 to 99.5. https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/107COVID-19 SMOTEtuning hyperparametersFilter-based feature selectionRandom ForestLogistic RegressionDecision tree |
| spellingShingle | mohammed Al-Hasnawi Abdulkareem Radhi Early stage prediction of COVID-19 Using machine learning model Wasit Journal of Computer and Mathematics Science COVID-19 SMOTE tuning hyperparameters Filter-based feature selection Random Forest Logistic Regression Decision tree |
| title | Early stage prediction of COVID-19 Using machine learning model |
| title_full | Early stage prediction of COVID-19 Using machine learning model |
| title_fullStr | Early stage prediction of COVID-19 Using machine learning model |
| title_full_unstemmed | Early stage prediction of COVID-19 Using machine learning model |
| title_short | Early stage prediction of COVID-19 Using machine learning model |
| title_sort | early stage prediction of covid 19 using machine learning model |
| topic | COVID-19 SMOTE tuning hyperparameters Filter-based feature selection Random Forest Logistic Regression Decision tree |
| url | https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/107 |
| work_keys_str_mv | AT mohammedalhasnawi earlystagepredictionofcovid19usingmachinelearningmodel AT abdulkareemradhi earlystagepredictionofcovid19usingmachinelearningmodel |