The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis
Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Emergency Medicine International |
| Online Access: | http://dx.doi.org/10.1155/2020/7306435 |
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| _version_ | 1832568656024829952 |
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| author | Omer F. Akmese Gul Dogan Hakan Kor Hasan Erbay Emre Demir |
| author_facet | Omer F. Akmese Gul Dogan Hakan Kor Hasan Erbay Emre Demir |
| author_sort | Omer F. Akmese |
| collection | DOAJ |
| description | Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals. |
| format | Article |
| id | doaj-art-a23634b1489045068090b16121163c75 |
| institution | Kabale University |
| issn | 2090-2840 2090-2859 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Emergency Medicine International |
| spelling | doaj-art-a23634b1489045068090b16121163c752025-02-03T00:58:41ZengWileyEmergency Medicine International2090-28402090-28592020-01-01202010.1155/2020/73064357306435The Use of Machine Learning Approaches for the Diagnosis of Acute AppendicitisOmer F. Akmese0Gul Dogan1Hakan Kor2Hasan Erbay3Emre Demir4Department of Computer Technologies, University of Hitit, University of Kırıkkale, Çorum 19500, TurkeyDepartment of Surgical Medical Sciences, University of Hitit, Çorum 19040, TurkeyDepartment of Computer Technologies, University of Hitit, Çorum 19300, TurkeyDepartment of Computer Engineering, University of Turkish Aeronautical Association, Ankara 06790, TurkeyDepartment of Biostatistics, University of Hitit, Çorum 19040, TurkeyAcute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.http://dx.doi.org/10.1155/2020/7306435 |
| spellingShingle | Omer F. Akmese Gul Dogan Hakan Kor Hasan Erbay Emre Demir The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis Emergency Medicine International |
| title | The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis |
| title_full | The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis |
| title_fullStr | The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis |
| title_full_unstemmed | The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis |
| title_short | The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis |
| title_sort | use of machine learning approaches for the diagnosis of acute appendicitis |
| url | http://dx.doi.org/10.1155/2020/7306435 |
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