Medical decision support systems for diagnosing diseases based on ensemble learning algorithms
Diagnosing diseases in humans is the first step in treating diseases, and knowing it is important to determine treatment and deal with the disease in the correct way. Diagnosis is made in medical institutions using available tools and specialists in each medical field to determine the problem presen...
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| Format: | Article |
| Language: | English |
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Mosul University
2024-12-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
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| Online Access: | https://csmj.uomosul.edu.iq/article_185900_7aa090cb3b01443401f6274258966ef9.pdf |
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| _version_ | 1849702709640298496 |
|---|---|
| author | Luma Jarallah |
| author_facet | Luma Jarallah |
| author_sort | Luma Jarallah |
| collection | DOAJ |
| description | Diagnosing diseases in humans is the first step in treating diseases, and knowing it is important to determine treatment and deal with the disease in the correct way. Diagnosis is made in medical institutions using available tools and specialists in each medical field to determine the problem presented by the patient. Modeling and analysis of medical data is important in healthcare and social applications in areas related to disease prediction and diagnosis. The model selection strategy is an important determinant of the performance and acceptance of a medical diagnostic decision support system. This paper proposes a stacked learning model derived from multiple ensembles learning algorithms, including Random Forest, Catboost and XGBoost. To determine the effectiveness of the model, it was tested using eight data sets covering different diseases to help make disease diagnosis decisions. The results show that the proposed model generally outperforms individual machine learning models in terms of accuracy |
| format | Article |
| id | doaj-art-12e8bce2f56d4360bcc41b7a319d5798 |
| institution | DOAJ |
| issn | 1815-4816 2311-7990 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Mosul University |
| record_format | Article |
| series | Al-Rafidain Journal of Computer Sciences and Mathematics |
| spelling | doaj-art-12e8bce2f56d4360bcc41b7a319d57982025-08-20T03:17:32ZengMosul UniversityAl-Rafidain Journal of Computer Sciences and Mathematics1815-48162311-79902024-12-0118211512010.33899/csmj.2024.150271.1131185900Medical decision support systems for diagnosing diseases based on ensemble learning algorithmsLuma Jarallah0College of Administration and Economics Department of Management Information Systems, Mosul University, IraqDiagnosing diseases in humans is the first step in treating diseases, and knowing it is important to determine treatment and deal with the disease in the correct way. Diagnosis is made in medical institutions using available tools and specialists in each medical field to determine the problem presented by the patient. Modeling and analysis of medical data is important in healthcare and social applications in areas related to disease prediction and diagnosis. The model selection strategy is an important determinant of the performance and acceptance of a medical diagnostic decision support system. This paper proposes a stacked learning model derived from multiple ensembles learning algorithms, including Random Forest, Catboost and XGBoost. To determine the effectiveness of the model, it was tested using eight data sets covering different diseases to help make disease diagnosis decisions. The results show that the proposed model generally outperforms individual machine learning models in terms of accuracyhttps://csmj.uomosul.edu.iq/article_185900_7aa090cb3b01443401f6274258966ef9.pdfmedical decision supportdisease diagnosismachine learningcollective learning |
| spellingShingle | Luma Jarallah Medical decision support systems for diagnosing diseases based on ensemble learning algorithms Al-Rafidain Journal of Computer Sciences and Mathematics medical decision support disease diagnosis machine learning collective learning |
| title | Medical decision support systems for diagnosing diseases based on ensemble learning algorithms |
| title_full | Medical decision support systems for diagnosing diseases based on ensemble learning algorithms |
| title_fullStr | Medical decision support systems for diagnosing diseases based on ensemble learning algorithms |
| title_full_unstemmed | Medical decision support systems for diagnosing diseases based on ensemble learning algorithms |
| title_short | Medical decision support systems for diagnosing diseases based on ensemble learning algorithms |
| title_sort | medical decision support systems for diagnosing diseases based on ensemble learning algorithms |
| topic | medical decision support disease diagnosis machine learning collective learning |
| url | https://csmj.uomosul.edu.iq/article_185900_7aa090cb3b01443401f6274258966ef9.pdf |
| work_keys_str_mv | AT lumajarallah medicaldecisionsupportsystemsfordiagnosingdiseasesbasedonensemblelearningalgorithms |