Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques
Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to c...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/3/492 |
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| author | Marwa Elseddik Reham R. Mostafa Ahmed Elashry Nora El-Rashidy Shaker El-Sappagh Shimaa Elgamal Ahmed Aboelfetouh Hazem El-Bakry |
| author_facet | Marwa Elseddik Reham R. Mostafa Ahmed Elashry Nora El-Rashidy Shaker El-Sappagh Shimaa Elgamal Ahmed Aboelfetouh Hazem El-Bakry |
| author_sort | Marwa Elseddik |
| collection | DOAJ |
| description | Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman’s correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery. |
| first_indexed | 2024-03-11T09:48:02Z |
| format | Article |
| id | doaj.art-534e3e8e0fcb49f4b5ed2d9dec42e2b5 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T09:48:02Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-534e3e8e0fcb49f4b5ed2d9dec42e2b52023-11-16T16:25:30ZengMDPI AGDiagnostics2075-44182023-01-0113349210.3390/diagnostics13030492Predicting CTS Diagnosis and Prognosis Based on Machine Learning TechniquesMarwa Elseddik0Reham R. Mostafa1Ahmed Elashry2Nora El-Rashidy3Shaker El-Sappagh4Shimaa Elgamal5Ahmed Aboelfetouh6Hazem El-Bakry7Department of the Robotics and Internet Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El Sheikh 33516, EgyptDepartment of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, EgyptDepartment of Information Systems, Faculty of Computers and Information, Kafrelsheiksh University, Kafr El Sheikh 33516, EgyptDepartment of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafr El Sheikh 33516, EgyptFaculty of Computer Science and Engineering, Galala University, Suez 43511, EgyptDepartment of Neuropsychiatry, Faculty of Medicine, Kafrelsheiksh University, Kafr El Sheikh 33516, EgyptDepartment of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, EgyptDepartment of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, EgyptCarpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman’s correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery.https://www.mdpi.com/2075-4418/13/3/492carpal tunnel syndrome (CTS)machine learning (ML)ultrasonography (US)nerve condition studies (NCS)Boston Carpal Tunnel Syndrome Questionnaire (BCTQ) |
| spellingShingle | Marwa Elseddik Reham R. Mostafa Ahmed Elashry Nora El-Rashidy Shaker El-Sappagh Shimaa Elgamal Ahmed Aboelfetouh Hazem El-Bakry Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques Diagnostics carpal tunnel syndrome (CTS) machine learning (ML) ultrasonography (US) nerve condition studies (NCS) Boston Carpal Tunnel Syndrome Questionnaire (BCTQ) |
| title | Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques |
| title_full | Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques |
| title_fullStr | Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques |
| title_full_unstemmed | Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques |
| title_short | Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques |
| title_sort | predicting cts diagnosis and prognosis based on machine learning techniques |
| topic | carpal tunnel syndrome (CTS) machine learning (ML) ultrasonography (US) nerve condition studies (NCS) Boston Carpal Tunnel Syndrome Questionnaire (BCTQ) |
| url | https://www.mdpi.com/2075-4418/13/3/492 |
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