{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T22:30:15Z","timestamp":1780093815405,"version":"3.54.0"},"reference-count":64,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008982","name":"Qatar National Research Fund","doi-asserted-by":"publisher","award":["NPRP12S-0227-190164"],"award-info":[{"award-number":["NPRP12S-0227-190164"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004252","name":"Qatar University","doi-asserted-by":"publisher","award":["IRCC-2021-001"],"award-info":[{"award-number":["IRCC-2021-001"]}],"id":[{"id":"10.13039\/501100004252","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004515","name":"National University of Malaysia","doi-asserted-by":"publisher","award":["DPK-2021-001"],"award-info":[{"award-number":["DPK-2021-001"]}],"id":[{"id":"10.13039\/501100004515","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter\u2014the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset.<\/jats:p>","DOI":"10.3390\/s22051793","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7068-9112","authenticated-orcid":false,"given":"Amith","family":"Khandakar","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"},{"name":"Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0744-8206","authenticated-orcid":false,"given":"Muhammad E. H.","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mamun Bin Ibne","family":"Reaz","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4819-863X","authenticated-orcid":false,"given":"Sawal Hamid Md","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8425-4029","authenticated-orcid":false,"given":"Tariq O.","family":"Abbas","sequence":"additional","affiliation":[{"name":"Urology Division, Surgery Department, Sidra Medicine, Doha 26999, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7033-3693","authenticated-orcid":false,"given":"Tanvir","family":"Alam","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8663-886X","authenticated-orcid":false,"given":"Mohamed Arselene","family":"Ayari","sequence":"additional","affiliation":[{"name":"College of Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zaid B.","family":"Mahbub","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Physics, North South University, Dhaka 1000, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rumana","family":"Habib","sequence":"additional","affiliation":[{"name":"Neurology Department, BIRDEM General Hospital, Dhaka 1000, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6938-6496","authenticated-orcid":false,"given":"Tawsifur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anas M.","family":"Tahir","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9060-0346","authenticated-orcid":false,"given":"Ahmad Ashrif A.","family":"Bakar","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7188-8903","authenticated-orcid":false,"given":"Rayaz A.","family":"Malik","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine-Qatar, Ar-Rayyan 24144, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","unstructured":"Cho, N., Kirigia, J., Mbanya, J., Ogurstova, K., Guariguata, L., and Rathmann, W. (2015). IDF Diabetes Atlas-8th. Int. Diabetes Fed., 160."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1093\/ptj\/68.12.1887","article-title":"Risk factors in the diabetic foot: Recognition and management","volume":"68","author":"Sims","year":"1988","journal-title":"Phys. Ther."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2193","DOI":"10.2337\/dc09-0651","article-title":"History of foot ulcer increases mortality among individuals with diabetes: Ten-year follow-up of the Nord-Tr\u00f8ndelag Health Study, Norway","volume":"32","author":"Iversen","year":"2009","journal-title":"Diabetes Care"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e12460","DOI":"10.2196\/12460","article-title":"Continuous temperature-monitoring socks for home use in patients with diabetes: Observational study","volume":"20","author":"Reyzelman","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"973","DOI":"10.2337\/dc16-2294","article-title":"Feasibility and efficacy of a smart mat technology to predict development of diabetic plantar ulcers","volume":"40","author":"Frykberg","year":"2017","journal-title":"Diabetes Care"},{"key":"ref_6","unstructured":"Inagaki Nagase, F.N. (2017). The Impact of Diabetic Foot Problems on Health-Related Quality of Life Of People with Diabetes. [Master\u2019s Thesis, University of Alberta]."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1177\/1932296819854062","article-title":"Infrared 3D thermography for inflammation detection in diabetic foot disease: A proof of concept","volume":"14","year":"2020","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"430","DOI":"10.21037\/atm.2017.08.40","article-title":"Remote home monitoring to identify and prevent diabetic foot ulceration","volume":"5","author":"Crisologo","year":"2017","journal-title":"Ann. Transl. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103754","DOI":"10.1016\/j.infrared.2021.103754","article-title":"Infrared machine vision and infrared thermography with deep learning: A review","volume":"116","author":"He","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.infrared.2016.07.013","article-title":"Narrative review: Diabetic foot and infrared thermography","volume":"78","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"918","DOI":"10.2337\/diacare.14.10.918","article-title":"Contact thermography of painful diabetic neuropathic foot","volume":"14","author":"Chan","year":"1991","journal-title":"Diabetes Care"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/42.746635","article-title":"A reappraisal of the use of infrared thermal image analysis in medicine","volume":"17","author":"Jones","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kaabouch, N., Chen, Y., Anderson, J., Ames, F., and Paulson, R. (2009, January 19\u201320). Asymmetry Analysis Based on Genetic Algorithms for the Prediction of Foot Ulcers. In Proceedings of Visualization and Data Analysis, San Jose, CA, USA.","DOI":"10.1117\/12.805975"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"013012","DOI":"10.1117\/1.3553240","article-title":"Enhancement of the asymmetry-based overlapping analysis through features extraction","volume":"20","author":"Kaabouch","year":"2011","journal-title":"J. Electron. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"026003","DOI":"10.1117\/1.JBO.20.2.026003","article-title":"Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis","volume":"20","author":"Liu","year":"2015","journal-title":"J. Biomed. Opt."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.infrared.2015.09.022","article-title":"Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum","volume":"73","year":"2015","journal-title":"Infrared Phys. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.infrared.2017.01.010","article-title":"A quantitative index for classification of plantar thermal changes in the diabetic foot","volume":"81","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"28383","DOI":"10.1109\/ACCESS.2019.2902502","article-title":"Statistical approximation of plantar temperature distribution on diabetic subjects based on beta mixture model","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","first-page":"1005","article-title":"A survey on image classification approaches and techniques","volume":"2","author":"Kamavisdar","year":"2013","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.knosys.2011.07.016","article-title":"ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging","volume":"26","author":"Ren","year":"2012","journal-title":"Knowl. Based Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"161296","DOI":"10.1109\/ACCESS.2019.2951356","article-title":"Plantar thermogram database for the study of diabetic foot complications","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104838","DOI":"10.1016\/j.compbiomed.2021.104838","article-title":"A machine learning model for early detection of diabetic foot using thermogram images","volume":"137","author":"Khandakar","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cruz-Vega, I., Hernandez-Contreras, D., Peregrina-Barreto, H., Rangel-Magdaleno, J.d.J., and Ramirez-Cortes, J.M. (2020). Deep Learning Classification for Diabetic Foot Thermograms. Sensors, 20.","DOI":"10.3390\/s20061762"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tawsifur Rahman, A.K., Qiblawey, Y., Tahir, A., Kiranyaz, S., Saad, M.T.I., Kashem, B.A., Al Maadeed, S., Zughaier, S.M., Chowdhury, M.E.H., and Khan, M.S. (2020). Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images. arXiv.","DOI":"10.1016\/j.compbiomed.2021.104319"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/0007-1226(92)90063-4","article-title":"Angiosome theory","volume":"45","author":"Taylor","year":"1992","journal-title":"Br. J. Plast. Surg."},{"key":"ref_27","unstructured":"Cajacuri, L.A.V. (2013). Early Diagnostic of Diabetic Foot Using Thermal Images. [Ph.D. Thesis, Universit\u00e9 D\u2019Orl\u00e9ans]."},{"key":"ref_28","unstructured":"Flir, T. (2022, January 08). How Does Emissivity Affect Thermal Imaging?. Available online: https:\/\/www.flir.eu\/discover\/professional-tools\/how-does-emissivity-affect-thermal-imaging\/."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1109\/42.14513","article-title":"An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement","volume":"7","author":"Zimmerman","year":"1988","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","article-title":"Can AI help in screening viral and COVID-19 pneumonia?","volume":"8","author":"Chowdhury","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","unstructured":"Tahir, A., Qiblawey, Y., Khandakar, A., Rahman, T., Khurshid, U., Musharavati, F., Kiranyaz, S., and Chowdhury, M.E. (2020). Coronavirus: Comparing COVID-19, SARS and MERS in the eyes of AI. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"191586","DOI":"10.1109\/ACCESS.2020.3031384","article-title":"Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization","volume":"8","author":"Rahman","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rahman, T., Chowdhury, M.E., Khandakar, A., Islam, K.R., Islam, K.F., Mahbub, Z.B., Kadir, M.A., and Kashem, S. (2020). Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray. Appl. Sci., 10.","DOI":"10.3390\/app10093233"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1007\/s10916-018-1088-1","article-title":"Medical image analysis using convolutional neural networks: A review","volume":"42","author":"Anwar","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. In Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3211","DOI":"10.1109\/TIM.2018.2872387","article-title":"Characterization of $ S_1 $ and $ S_2 $ Heart Sounds Using Stacked Autoencoder and Convolutional Neural Network","volume":"68","author":"Mishra","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_38","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103219","DOI":"10.1016\/j.infrared.2020.103219","article-title":"Computer aided detection of diabetic foot ulcer using asymmetry analysis of texture and temperature features","volume":"105","author":"Saminathan","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.E., Alzoubi, K., Khandakar, A., Khallifa, R., Abouhasera, R., Koubaa, S., Ahmed, R., and Hasan, A. (2019). Wearable real-time heart attack detection and warning system to reduce road accidents. Sensors, 19.","DOI":"10.3390\/s19122780"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.E., Khandakar, A., Alzoubi, K., Mansoor, S., M Tahir, A., Reaz, M.B.I., and Al-Emadi, N. (2019). Real-Time Smart-Digital stethoscope system for heart diseases monitoring. Sensors, 19.","DOI":"10.3390\/s19122781"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.H., Shuzan, M.N.I., Chowdhury, M.E., Mahbub, Z.B., Uddin, M.M., Khandakar, A., and Reaz, M.B.I. (2020). Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques. Sensors, 20.","DOI":"10.3390\/s20113127"},{"key":"ref_44","unstructured":"Hall, M.A. (1999). Correlation-based feature selection for machine learning. [Ph.D. Thesis, The University of Waikato]."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"151482","DOI":"10.1109\/ACCESS.2019.2947701","article-title":"Combining multiple feature-ranking techniques and clustering of variables for feature selection","volume":"7","author":"Haq","year":"2019","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.2174\/2212392XMTA2bMjko1","article-title":"MRMD2. 0: A python tool for machine learning with feature ranking and reduction","volume":"15","author":"He","year":"2020","journal-title":"Curr. Bioinform."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Rahman, T., Al-Ishaq, F.A., Al-Mohannadi, F.S., Mubarak, R.S., Al-Hitmi, M.H., Islam, K.R., Khandakar, A., Hssain, A.A., Al-Madeed, S., and Zughaier, S.M. (2021). Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique. Diagnostics, 11.","DOI":"10.3390\/diagnostics11091582"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"120422","DOI":"10.1109\/ACCESS.2021.3105321","article-title":"Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 using Complete Blood Count Parameters","volume":"9","author":"Rahman","year":"2021","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Shi, X., Li, Q., Qi, Y., Huang, T., and Li, J. (2017, January 24\u201326). An Accident Prediction Approach Based on XGBoost. In Proceedings of 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, China.","DOI":"10.1109\/ISKE.2017.8258806"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sharaff, A., and Gupta, H. (2019). Extra-tree classifier with metaheuristics approach for email classification. Advances in Computer Communication and Computational Sciences, Springer.","DOI":"10.1007\/978-981-13-6861-5_17"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"94625","DOI":"10.1109\/ACCESS.2021.3092840","article-title":"Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms","volume":"9","author":"Rahman","year":"2021","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"96775","DOI":"10.1109\/ACCESS.2021.3095380","article-title":"A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model","volume":"9","author":"Shuzan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_55","unstructured":"(2021, March 02). Multilayer Perceptron. Available online: https:\/\/en.wikipedia.org\/wiki\/Multilayer_perceptron."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, Y. (2012, January 14\u201316). Support vector machine classification algorithm and its application. In Proceedings of International Conference on Information Computing and Applications, Chengde, China.","DOI":"10.1007\/978-3-642-34041-3_27"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Bahad, P., and Saxena, P. (2020). Study of Adaboost and Gradient Boosting Algorithms for Predictive Analytics. Proceedings of International Conference on Intelligent Computing and Smart Communication 2019, Springer.","DOI":"10.1007\/978-981-15-0633-8_22"},{"key":"ref_58","unstructured":"(2021, March 02). Logistic Regression. Available online: https:\/\/en.wikipedia.org\/wiki\/Logistic_regression."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/S0167-4048(02)00514-X","article-title":"Use of k-nearest neighbor classifier for intrusion detection","volume":"21","author":"Liao","year":"2002","journal-title":"Comput. Secur."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Bobkov, V., Bobkova, A., Porshnev, S., and Zuzin, V. (2016, January 15\u201317). The Application of Ensemble Learning for Delineation of the Left Ventricle on Echocardiographic Records. In Proceedings of 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics), Omsk, Russia.","DOI":"10.1109\/Dynamics.2016.7818984"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Gu, Q., Li, Z., and Han, J. (2011). Linear Discriminant Dimensionality Reduction. Joint European conference on Machine Learning and Knowledge Discovery in Databases, Springer.","DOI":"10.1007\/978-3-642-23780-5_45"},{"key":"ref_62","first-page":"1","article-title":"Xgboost: Extreme gradient boosting","volume":"1","author":"Chen","year":"2015","journal-title":"R Package Version 0.4\u20132"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Taha, A.A., and Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Med. Imaging, 15.","DOI":"10.1186\/s12880-015-0068-x"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"B\u00e9n\u00e9dict, G., Koops, V., Odijk, D., and de Rijke, M. (2021). sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification. arXiv.","DOI":"10.1145\/3606375"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1793\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:26:49Z","timestamp":1760135209000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,24]]},"references-count":64,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22051793"],"URL":"https:\/\/doi.org\/10.3390\/s22051793","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,24]]}}}