{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:26:43Z","timestamp":1792794403465,"version":"4.5.0"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Projects Coordination Unit of Yozgat Bozok University","award":["FED-2026-2261"],"award-info":[{"award-number":["FED-2026-2261"]}]},{"award":["FED-2026-2261"],"award-info":[{"award-number":["FED-2026-2261"]}],"id":[{"id":"https:\/\/ror.org\/04qvdf239","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: This paper aims to design an explainable and accurate ML framework to support the automatic classification of Carpal Tunnel Syndrome (CTS) severity from structured patient data. Methods: For the experiment, an open-source dataset of 1037 samples was used. Following stratified partitioning, 305 samples were held out as the test set; the remaining training set (n = 732) was augmented to 1216 balanced samples via ADASYN, yielding an 80\/20 train\/test ratio relative to the final dataset (n = 1521). In order to solve the problem of imbalance associated with CTS cases of moderate and severe severity, the Adaptive Synthetic Sampling (ADASYN) technique was employed. The model\u2019s predictive capacity was increased by means of feature engineering methods, such as polynomial transformations and clinically relevant interactions. Specifically, four ensemble learning models (XGBoost, Random Forest, LightGBM, and CatBoost) were optimized and ensembled with the use of a stacking approach with a base algorithm of LightGBM. The explainability of the model was ensured through SHAP and LIME analysis. Results: As a result, the stacking ensemble was able to reach a test accuracy of 91.15%, an F1-score of 91.13%, and an ROC-AUC of 0.9708. The proposed ensemble performed superiorly compared to any other individual algorithm while having stable performance across all severity categories. Conclusions: Through the explainability analysis, it was observed that such a classification model relies on important clinically relevant predictors, including cross-sectional area (CSA), duration of symptoms, pain level measured by the numeric rating scale of pain (NRS), and palmar bowing (PB).<\/jats:p>","DOI":"10.3390\/diagnostics16111604","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T06:42:18Z","timestamp":1779691338000},"page":"1604","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Explainable Ensemble Learning for Robust Severity Stratification of Carpal Tunnel Syndrome from Clinical Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Muhammet Emin","family":"Sahin","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Izmir Bakircay University, 35665 Izmir, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3922-934X","authenticated-orcid":false,"given":"Hasan","family":"Ulutas","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Yozgat Bozok University, 66100 Yozgat, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murat","family":"Korkmaz","sequence":"additional","affiliation":[{"name":"Department of Orthopedics and Traumatology, Faculty of Medicine, Yozgat Bozok University, 66100 Yozgat, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mucella","family":"Ozbay Karakus","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Izmir Bakircay University, 35665 Izmir, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Orhan","family":"Er","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Izmir Bakircay University, 35665 Izmir, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huriye","family":"Unluel","sequence":"additional","affiliation":[{"name":"Department of Neurology, Faculty of Medicine, Yozgat Bozok University, 66100 Yozgat, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/BF02048482","article-title":"Neurophysiological classification of carpal tunnel syndrome: Assessment of 600 symptomatic hands","volume":"18","author":"Padua","year":"1997","journal-title":"Ital. J. Neurol. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.2106\/00004623-199311000-00002","article-title":"A self-administered questionnaire for the assessment of severity of symptoms and functional status in carpal tunnel syndrome","volume":"75","author":"Simmons","year":"1993","journal-title":"JBJS"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1002\/mus.23389","article-title":"Evidence-based guideline: Neuromuscular ultrasound for the diagnosis of carpal tunnel syndrome","volume":"46","author":"Cartwright","year":"2012","journal-title":"Muscle Nerve"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1016\/j.ultrasmedbio.2011.06.011","article-title":"Diagnostic Accuracy of Ultrasonography, V.S. Electromyography in Carpal Tunnel Syndrome: A Systematic Review of Literature","volume":"37","author":"Roll","year":"2011","journal-title":"Ultrasound Med. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1148\/radiol.2501080397","article-title":"Carpal tunnel syndrome assessment with US: Value of additional cross-sectional area measurements of the median nerve in patients versus healthy volunteers","volume":"250","author":"Klauser","year":"2009","journal-title":"Radiology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"17464","DOI":"10.1038\/s41598-021-97043-7","article-title":"Machine learning-based approach for disease severity classification of carpal tunnel syndrome","volume":"11","author":"Park","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Elseddik, M., Mostafa, R.R., Elashry, A., El-Rashidy, N., El-Sappagh, S., Elgamal, S., and El-Bakry, H. (2023). Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques. Diagnostics, 13.","DOI":"10.3390\/diagnostics13030492"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Inui, A., Takase, F., Lucchina, S., and Kanatani, T. (2025). Prediction of Electrophysiological Severity and Carpal Tunnel Syndrome Instrument Changes After Carpal Tunnel Release Using Machine Learning Model. Appl. Sci., 15.","DOI":"10.3390\/app15041812"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kharat, P.P., Al Majmaie, S., Ghajari, G., Amsaad, F., and Ibrahem, M.I. (2026). A Secure and Robust ML Framework for Sequence Classification and Adversarial Evaluation in a Bilateral Carpal Tunnel Syndrome Crossover Dataset. Information, 17.","DOI":"10.3390\/info17030293"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102264","DOI":"10.1016\/j.hansur.2025.102264","article-title":"Artificial Intelligence and Carpal Tunnel Syndrome: A systematic review and contemporary update on imaging techniques","volume":"44","author":"Misch","year":"2025","journal-title":"Hand Surg. Rehabil."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bakalis, D., Kontogiannis, P., Ntais, E., Simos, Y.V., Tsamis, K.I., and Manis, G. (2024). Carpal Tunnel Syndrome Automated Diagnosis: A Motor vs. Sensory Nerve Conduction-Based Approach. Bioengineering, 11.","DOI":"10.3390\/bioengineering11020175"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Thakur, N.V., Yenurkar, G.K., Aherrao, A., Aherrao, A., Landge, S., and Katre, S. (2023). Medical Image Fusion Using Discrete Wavelet Transform: In view of Deep Learning. Proceedings of the 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI, Wardha, India, IEEE.","DOI":"10.1109\/IDICAIEI58380.2023.10406337"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Elseddik, M., Alnowaiser, K., Mostafa, R.R., Elashry, A., El-Rashidy, N., Elgamal, S., and El-Bakry, H. (2023). Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome. Diagnostics, 13.","DOI":"10.3390\/diagnostics13203211"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yeti\u015f, M., Kocaman, H., Canli, M., Yildirim, H., Yeti\u015f, A., and Ceylan, I. (2024). Carpal tunnel syndrome prediction with machine learning algorithms using anthropometric and strength-based measurement. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0300044"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sasaki, T., Koyama, T., Kuroiwa, T., Nimura, A., Okawa, A., Wakabayashi, Y., and Fujita, K. (2022). Evaluation of the Existing Electrophysiological Severity Classifications in Carpal Tunnel Syndrome. J. Clin. Med., 11.","DOI":"10.3390\/jcm11061685"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pellicer-Valero, O.J., Mart\u00edn-Guerrero, J.D., Fern\u00e1ndez-de-las-Pe\u00f1as, C., De-la-Llave-Rinc\u00f3n, A.I., Rodr\u00edguez-Jim\u00e9nez, J., Navarro-Pardo, E., and Cigar\u00e1n-M\u00e9ndez, M.I. (2021). Spectral Clustering Reveals Different Profiles of Central Sensitization in Women with Carpal Tunnel Syndrome. Symmetry, 13.","DOI":"10.3390\/sym13061042"},{"key":"ref_17","first-page":"76","article-title":"Towards Diagnosis of Carpal Tunnel Syndrome Using Machine Learning","volume":"7","author":"Wei","year":"2020","journal-title":"ACM Int. Conf. Proc. Ser."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1007\/s00256-024-04594-7","article-title":"Application of radiomics model based on ultrasound image features in the prediction of carpal tunnel syndrome severity","volume":"53","author":"Lyu","year":"2024","journal-title":"Skelet. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1080\/10255842.2024.2417200","article-title":"Use of machine learning methods in diagnosis of carpal tunnel syndrome","volume":"29","year":"2026","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1016\/j.ultrasmedbio.2022.05.030","article-title":"Using deep learning for ultrasound images to diagnose carpal tunnel syndrome with high accuracy","volume":"48","author":"Shinohara","year":"2022","journal-title":"Ultrasound Med. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.patrec.2020.02.020","article-title":"Diagnosis of carpal tunnel syndrome: A comparative study of shear wave elastography, morphometry and artificial intelligence techniques","volume":"133","author":"Ardakani","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1016\/j.ultrasmedbio.2020.03.017","article-title":"DeepNerve: A New Convolutional Neural Network for the Localization and Segmentation of the Median Nerve in Ultrasound Image Sequences","volume":"46","author":"Horng","year":"2020","journal-title":"Ultrasound Med. Biol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, C.H., Syu, W.T., Lin, M.T., Yeh, C.L., Boudier-Rev\u00e9ret, M., Hsiao, M.Y., and Kuo, P.L. (2021). Automated segmentation of median nerve in dynamic sonography using deep learning: Evaluation of model performance. Diagnostics, 11.","DOI":"10.3390\/diagnostics11101893"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101687","DOI":"10.1016\/j.compmedimag.2019.101687","article-title":"MNT-DeepSL: Median nerve tracking from carpal tunnel ultrasound images with deep similarity learning and analysis on continuous wrist motions","volume":"80","author":"Wang","year":"2020","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_25","first-page":"101687","article-title":"A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: Diagnostic case-control study","volume":"80","author":"Wang","year":"2021","journal-title":"JMIR Mhealth Uhealth"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tsamis, K.I., Kontogiannis, P., Gourgiotis, I., Ntabos, S., Sarmas, I., and Manis, G. (2021). Automatic electrodiagnosis of carpal tunnel syndrome using machine learning. Bioengineering, 8.","DOI":"10.3390\/bioengineering8110181"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e4279","DOI":"10.1097\/GOX.0000000000004279","article-title":"Developing Machine Learning Algorithms to Support Patient-centered, Value-based Carpal Tunnel Decompression Surgery","volume":"10","author":"Harrison","year":"2022","journal-title":"Plast. Reconstr. Surg. Glob. Open"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1007\/s10278-022-00661-4","article-title":"Deep CTS: A deep neural network for identification MRI of carpal tunnel syndrome","volume":"35","author":"Zhou","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1007\/s10278-025-01587-3","article-title":"Efficient Cerebral Infarction Segmentation Using U-Net and U-Net3 + Models","volume":"39","author":"Yuce","year":"2026","journal-title":"J. Imaging Inform. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary","volume":"61","author":"Herrera","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_32","unstructured":"He, H., Bai, Y., Garcia, E.A., and Li, S. (2008). ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning. Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), IEEE."},{"key":"ref_33","first-page":"793","article-title":"Machine learning-based clinical decision support using laboratory data","volume":"62","author":"Topcu","year":"2023","journal-title":"Clin. Chem. Lab. Med."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Masood, A., Naseem, U., Rashid, J., Kim, J., and Razzak, I. (2024). Review on enhancing clinical decision support system using machine learning. CAAI Trans. Intell. Technol., 1\u201314.","DOI":"10.1049\/cit2.12286"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1002\/ima.22849","article-title":"Image processing machine learning-based bone fracture detection classification using X-ray, images","volume":"33","author":"Sahin","year":"2023","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2019). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_38","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). Why Should I Trust You? Explaining the Predictions of Any Classifier. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, Association for Computational Linguistics.","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sirocchi, C., Bogliolo, A., and Montagna, S. (2024). Medical-informed machine learning: Integrating prior knowledge into medical decision systems. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02582-4"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1186\/s13054-024-04948-6","article-title":"A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: A step towards explainability","volume":"28","author":"Bomrah","year":"2024","journal-title":"Crit. Care"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","article-title":"Use of Ranks in One-Criterion Variance Analysis","volume":"47","author":"Kruskal","year":"1952","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_43","first-page":"1","article-title":"Statistical Comparisons of Classifiers over Multiple Data Sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/11\/1604\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T07:04:21Z","timestamp":1779692661000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/11\/1604"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,25]]},"references-count":43,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["diagnostics16111604"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16111604","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,25]]}}}