{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:34:46Z","timestamp":1769718886532,"version":"3.49.0"},"reference-count":22,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,10,4]]},"abstract":"<jats:p>The usage of cloud-based grid computing services and Internet of Things (IoT) devices in medical diagnoses is increasing enormously. The cloud service provider\u2019s data centers store vast amounts of data without processing it. This big data need some intelligent technique to analyze and classify heart disease from the considerable volume of data; it is a challenging task. Many deep learning techniques are introduced earlier for heart disease diagnosis in the literature study. Still, all other classification techniques failed to achieve the minimum loss in heart disease classification with the highest accuracy and faster performance. This research introduces a new classification approach to overcome these issues: elephant herding optimizer turned restricted Boltzmann machine EHO-RBM network. The optimizer is used in this network to optimize the number of neuron utilization during the learning process by updating the network weight without compromising the loss. The previous research proves that the optimizer is performed well in reaching global minima efficiently. Therefore, the new classifier incorporates the optimizers instead of the classical stochastic gradient descent optimizer to improve the network performance by minimizing the global minima faster with less loss in predicting heart disease. The simulation result of the new heart disease classification framework shows that the elephant herding optimizer-trained classification model has reduced the loss rate and maximized the accuracy rate up to 0.0027 then the comparison method. As a result, the new classifier has obtained a maximum accuracy of up to 99.96% .<\/jats:p>","DOI":"10.3233\/jifs-224275","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T12:03:33Z","timestamp":1689681813000},"page":"5383-5399","source":"Crossref","is-referenced-by-count":1,"title":["Internet of things (IoT) based heart disease classification framework using deep learning techniques"],"prefix":"10.1177","volume":"45","author":[{"given":"J. Shafiq","family":"Mansoor","sequence":"first","affiliation":[{"name":"Department of ECE, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India"}]},{"given":"Kamalraj","family":"Subramaniam","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-224275_ref1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/GLOCOM.2018.8647457","article-title":"Edge-Assisted Sensor Control in Healthcare IoT","author":"Amiri","year":"2018","journal-title":"2018 IEEE Global Communications Conference (GLOBECOM), AbuDhabi"},{"key":"10.3233\/JIFS-224275_ref2","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/ICITM.2017.7917920","article-title":"Health care monitoring system in Internet of Things (IoT) by using RFID","author":"Khan","year":"2017","journal-title":"2017 6th International Conference on Industrial Technology and Management (ICITM)"},{"key":"10.3233\/JIFS-224275_ref3","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/CBS.2018.8612282","article-title":"A Piezoelectret-based Flexible Sensor for Pulse Monitoring","author":"Zhang","year":"2018","journal-title":"2018 IEEE International Conference on Cyborg and Bionic Systems (CBS)"},{"key":"10.3233\/JIFS-224275_ref4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/GCAT47503.2019.8978381","article-title":"Motion Detection Using Image Processing","author":"Roy","year":"2019","journal-title":"2019 Global Conference for Advancement in Technology (GCAT)"},{"key":"10.3233\/JIFS-224275_ref5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ECACE.2019.8679246","article-title":"An Easy Approach to Develop a Digital Blood Pressure Meter","author":"Singha","year":"2019","journal-title":"2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox\u2019sBazar"},{"key":"10.3233\/JIFS-224275_ref6","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1109\/ICCES45898.2019.9002540","article-title":"ECG Monitoring System Using AD8232 Sensor","author":"Prasad","year":"2019","journal-title":"2019 International Conference on Communication and Electronics Systems (ICCES)"},{"key":"10.3233\/JIFS-224275_ref7","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1109\/ICOEI.2017.8300952","article-title":"Healthcare monitoring system using IoT","author":"Shaikh","year":"2017","journal-title":"2017 International Conference on Trends in Electronics and Informatics (ICEI)"},{"key":"10.3233\/JIFS-224275_ref8","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1109\/ICACCE.2018.8441752","article-title":"Health Monitoring & Management using IoT devices in a Cloud Based Framework","author":"Sharma","year":"2018","journal-title":"2018 International Conference on Advances in Computing and Communication Engineering (ICACCE)"},{"key":"10.3233\/JIFS-224275_ref9","doi-asserted-by":"publisher","first-page":"100203","DOI":"10.1016\/j.imu.2019.100203","article-title":"Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques","author":"Latha","year":"2019","journal-title":"Informatics in Medicine Unlocked"},{"key":"10.3233\/JIFS-224275_ref10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/access.2019.2923707","article-title":"Effective Heart Disease Prediction using Hybrid Machine Learning Techniques","author":"Mohan","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-224275_ref11","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1016\/j.procs.2017.11.283","article-title":"Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks","volume":"120","author":"Uyar","year":"2017","journal-title":"Procedia Computer Science"},{"key":"10.3233\/JIFS-224275_ref12","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-019-6721-5"},{"key":"10.3233\/JIFS-224275_ref13","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/978-981-10-5427-3_63","article-title":"Heart Disease Prediction System Using Random Forest","author":"Singh","year":"2017","journal-title":"Advances in Computing and Data Sciences"},{"key":"10.3233\/JIFS-224275_ref14","unstructured":"Zahra F. 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