{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T20:30:21Z","timestamp":1770150621414,"version":"3.49.0"},"reference-count":22,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T00:00:00Z","timestamp":1641686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009391","name":"University of Tabuk","doi-asserted-by":"publisher","award":["0186-1441-S"],"award-info":[{"award-number":["0186-1441-S"]}],"id":[{"id":"10.13039\/100009391","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient\u2019s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.<\/jats:p>","DOI":"10.3390\/s22020476","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"476","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1837-6797","authenticated-orcid":false,"given":"S.","family":"Manimurugan","sequence":"first","affiliation":[{"name":"Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia"}]},{"given":"Saad","family":"Almutairi","sequence":"additional","affiliation":[{"name":"Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7376-1458","authenticated-orcid":false,"given":"Majed Mohammed","family":"Aborokbah","sequence":"additional","affiliation":[{"name":"Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia"}]},{"given":"C.","family":"Narmatha","sequence":"additional","affiliation":[{"name":"Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0233-9940","authenticated-orcid":false,"given":"Subramaniam","family":"Ganesan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5396-8897","authenticated-orcid":false,"given":"Naveen","family":"Chilamkurti","sequence":"additional","affiliation":[{"name":"Department of Computer Science and IT, La Trobe University, Melbourne 3086, Australia"}]},{"given":"Riyadh A.","family":"Alzaheb","sequence":"additional","affiliation":[{"name":"Faculty of Applied Medical Sciences, University of Tabuk, Tabuk 47512, Saudi Arabia"}]},{"given":"Hani","family":"Almoamari","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,9]]},"reference":[{"key":"ref_1","first-page":"3660","article-title":"A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Health care","volume":"8","author":"Fatima","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","first-page":"3810","article-title":"Internet of Medical Things: A Review of Recent Contribution Dealing with Cyber-Physical System in Medicines","volume":"5","author":"Gatouillat","year":"2018","journal-title":"IEEE IoT J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Vishnu, S., Ramson, S.R.J., and Jegan, R. (2020, January 5\u20136). Internet of Medical Things (IoMT)\u2014An overview. Proceedings of the 5th International Conferences on Device, Circuit and Systems, Coimbatore, India.","DOI":"10.1109\/ICDCS48716.2020.243558"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.future.2019.12.028","article-title":"Securing Internet of Medical Things System: Limitations, Issue and Recommendation","volume":"105","author":"Yaacoub","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_5","first-page":"16921","article-title":"Deep Learning Method in Internet of Medical Things for Valvular Heart Diseases Screening Systems","volume":"8","author":"Su","year":"2021","journal-title":"IEEE IoT J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101079","DOI":"10.1109\/ACCESS.2020.2997831","article-title":"Edge-Cloud Computing and Artificial Intelligences in Internet of Medical Things: Architectures, Technology and Applications","volume":"8","author":"Sun","year":"2016","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"122259","DOI":"10.1109\/ACCESS.2020.3006424","article-title":"A Health Care Monitoring System for the Diagnosis of Heart Diseases in the IoMT Cloud Environments Using MSSO-ANFIS","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"135784","DOI":"10.1109\/ACCESS.2020.3007561","article-title":"An Efficient IoT-Based Patient Monitoring and Heart Diseases Predictions Systems Using Deep Learning Modified Neural Networks","volume":"8","author":"Simanta","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"34717","DOI":"10.1109\/ACCESS.2020.2974687","article-title":"An IoT Framework for Heart Diseases Predictions Based on MDCNN Classifier","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.comcom.2020.08.011","article-title":"Diagnosis of heart disease by a secure Internet of Health Things system based on Autoencoder Deep Neural Networks","volume":"162","author":"Deperlioglu","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"189503","DOI":"10.1109\/ACCESS.2020.3026214","article-title":"Enhanced Deep Learning Assisted Convolutional Neural Networks for Heart Diseases Predictions on the Internet of Medical Things Platforms","volume":"8","author":"Pan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"58006","DOI":"10.1109\/ACCESS.2020.2981337","article-title":"Optimal Features Selections-Based Medical Images Classifications Using Deep Learning Models in Internet of Medical Things","volume":"8","author":"Raj","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3409","DOI":"10.1007\/s13369-020-05105-1","article-title":"Prediction of Heart Diseases Using Deep Convolutional Neural Network","volume":"46","author":"Mehmood","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12145","DOI":"10.1007\/s00500-021-05865-4","article-title":"Real-time monitoring systems for early predictions of heart diseases using Internet of Things","volume":"25","author":"Basheer","year":"2021","journal-title":"Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.knosys.2018.12.031","article-title":"A feature selections approach for hyperspectral images based on modified ant lion optimizer","volume":"168","author":"Wang","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shashoa, N.A.A., Salem, N.A., Jleta, I.N., and Abusaeeda, O. (2016, January 19\u201321). Classification Depend on Linear Discriminant Analysis Using Desired Output. Proceedings of the International Conferences on Science and Technique of Automatic Controls & Computer Engineering, Sousse, Tunisia.","DOI":"10.1109\/STA.2016.7952041"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Network. Proceedings of the IEEE Conferences on Computer Visions and Pattern Recognitions, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_18","unstructured":"Ghoury, S., Sungur, C., and Durdu, A. (2019, January 26\u201328). Real-time disease detections of grapes and grape leaves using faster R-CNN and SSD MobileNet architecture. Proceedings of the International Conference on Advanced Technologies, Computer Engineering and Sciences, Alanya, Turkey."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sharma, S., and Parmar, M. (2020). Heart diseases prediction using deep learning neural network model. Int. J. Innov. Technol. Explor. Eng., 9.","DOI":"10.35940\/ijitee.C9009.019320"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.inffus.2020.06.008","article-title":"A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion","volume":"63","author":"Ali","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_21","unstructured":"Paul, S.M.V., Balasubramaniam, S., Panchatcharam, P., Malarvizhi Kumar, P., and Mubarakali, A. (2021). Intelligent Framework for Prediction of Heart Disease using Deep Learning. Arab. J. Sci. Eng., 1\u201311."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/s41746-018-0065-x","article-title":"Deep echocardiography: Data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease","volume":"1","author":"Madani","year":"2018","journal-title":"NPJ Digit. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/476\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:01:47Z","timestamp":1760364107000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/476"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,9]]},"references-count":22,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020476"],"URL":"https:\/\/doi.org\/10.3390\/s22020476","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,9]]}}}