Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning
Soft Tissue Tumors (STT) are a form of sarcoma found in tissues that connect, support, and surround body structures. Because of their shallow frequency in the body and their great diversity, they appear to be heterogeneous when observed through Magnetic Resonance Imaging (MRI). They are easily confu...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2021-03-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2020.9020023 |
| _version_ | 1811344553727229952 |
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| author | El Arbi Abdellaoui Alaoui Stéphane Cédric Koumetio Tekouabou Sri Hartini Zuherman Rustam Hassan Silkan Said Agoujil |
| author_facet | El Arbi Abdellaoui Alaoui Stéphane Cédric Koumetio Tekouabou Sri Hartini Zuherman Rustam Hassan Silkan Said Agoujil |
| author_sort | El Arbi Abdellaoui Alaoui |
| collection | DOAJ |
| description | Soft Tissue Tumors (STT) are a form of sarcoma found in tissues that connect, support, and surround body structures. Because of their shallow frequency in the body and their great diversity, they appear to be heterogeneous when observed through Magnetic Resonance Imaging (MRI). They are easily confused with other diseases such as fibroadenoma mammae, lymphadenopathy, and struma nodosa, and these diagnostic errors have a considerable detrimental effect on the medical treatment process of patients. Researchers have proposed several machine learning models to classify tumors, but none have adequately addressed this misdiagnosis problem. Also, similar studies that have proposed models for evaluation of such tumors mostly do not consider the heterogeneity and the size of the data. Therefore, we propose a machine learning-based approach which combines a new technique of preprocessing the data for features transformation, resampling techniques to eliminate the bias and the deviation of instability and performing classifier tests based on the Support Vector Machine (SVM) and Decision Tree (DT) algorithms. The tests carried out on dataset collected in Nur Hidayah Hospital of Yogyakarta in Indonesia show a great improvement compared to previous studies. These results confirm that machine learning methods could provide efficient and effective tools to reinforce the automatic decision-making processes of STT diagnostics. |
| first_indexed | 2024-04-13T19:49:02Z |
| format | Article |
| id | doaj.art-3dd288dd6bf64bbd9c49a6af4e36d131 |
| institution | Directory Open Access Journal |
| issn | 2096-0654 |
| language | English |
| last_indexed | 2024-04-13T19:49:02Z |
| publishDate | 2021-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj.art-3dd288dd6bf64bbd9c49a6af4e36d1312022-12-22T02:32:36ZengTsinghua University PressBig Data Mining and Analytics2096-06542021-03-0141334610.26599/BDMA.2020.9020023Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine LearningEl Arbi Abdellaoui Alaoui0Stéphane Cédric Koumetio Tekouabou1Sri Hartini2Zuherman Rustam3Hassan Silkan4Said Agoujil5<institution content-type="dept">Department of Computer Sciences, Faculty of Sciences and Technologies</institution>, <institution>My Ismail University</institution>, <city>Errachidia</city> <postal-code>52000</postal-code>, <country>Morocco</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>Faculty of Sciences, Chouaib Doukkali University</institution>, <city>El Jadida</city> <postal-code>24000</postal-code>, <country>Morocco</country>.<institution content-type="dept">Department of Mathematics</institution>, <institution>Universitas Indonesia</institution>, <city>Depok</city> <postal-code>16424</postal-code>, <country>Indonesia</country>.<institution content-type="dept">Department of Mathematics</institution>, <institution>Universitas Indonesia</institution>, <city>Depok</city> <postal-code>16424</postal-code>, <country>Indonesia</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>Faculty of Sciences, Chouaib Doukkali University</institution>, <city>El Jadida</city> <postal-code>24000</postal-code>, <country>Morocco</country>.<institution content-type="dept">Department of Computer Sciences, Faculty of Sciences and Technologies</institution>, <institution>My Ismail University</institution>, <city>Errachidia</city> <postal-code>52000</postal-code>, <country>Morocco</country>.Soft Tissue Tumors (STT) are a form of sarcoma found in tissues that connect, support, and surround body structures. Because of their shallow frequency in the body and their great diversity, they appear to be heterogeneous when observed through Magnetic Resonance Imaging (MRI). They are easily confused with other diseases such as fibroadenoma mammae, lymphadenopathy, and struma nodosa, and these diagnostic errors have a considerable detrimental effect on the medical treatment process of patients. Researchers have proposed several machine learning models to classify tumors, but none have adequately addressed this misdiagnosis problem. Also, similar studies that have proposed models for evaluation of such tumors mostly do not consider the heterogeneity and the size of the data. Therefore, we propose a machine learning-based approach which combines a new technique of preprocessing the data for features transformation, resampling techniques to eliminate the bias and the deviation of instability and performing classifier tests based on the Support Vector Machine (SVM) and Decision Tree (DT) algorithms. The tests carried out on dataset collected in Nur Hidayah Hospital of Yogyakarta in Indonesia show a great improvement compared to previous studies. These results confirm that machine learning methods could provide efficient and effective tools to reinforce the automatic decision-making processes of STT diagnostics.https://www.sciopen.com/article/10.26599/BDMA.2020.9020023classificationsoft tissues tumourspreprocessing techniquessupport vector machine (svm)decision tree (dt)machine learningpredictive diagnosis |
| spellingShingle | El Arbi Abdellaoui Alaoui Stéphane Cédric Koumetio Tekouabou Sri Hartini Zuherman Rustam Hassan Silkan Said Agoujil Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning Big Data Mining and Analytics classification soft tissues tumours preprocessing techniques support vector machine (svm) decision tree (dt) machine learning predictive diagnosis |
| title | Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning |
| title_full | Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning |
| title_fullStr | Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning |
| title_full_unstemmed | Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning |
| title_short | Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning |
| title_sort | improvement in automated diagnosis of soft tissues tumors using machine learning |
| topic | classification soft tissues tumours preprocessing techniques support vector machine (svm) decision tree (dt) machine learning predictive diagnosis |
| url | https://www.sciopen.com/article/10.26599/BDMA.2020.9020023 |
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