Predicting patient health status with an artificial intelligence-based framework for internet of medical things
DOI:
https://doi.org/10.65746/jbrha111Keywords:
internet of medical things, deep belief network, ensemble learning, support vector machines, feedforward neural network, naive bayesAbstract
The Internet of Medical Things (IoMT) is one technology quite likely to change healthcare. Combining medical devices with the Internet of Things (IoT) permits them to be remote patient health monitors. Still, the precise expectation of patient health issues based on IoMT technology remains a difficult task. This present work intends to solve this challenge by means of an ensemble Deep Belief Network (DBN) framework, which incorporates Support Vector Machines (SVM), Feedforward Neural Networks (FFNN), Naive Bayes (NB), and the Deep Belief Network (DBN). This project intends to create a solid framework based on IoMT data that can reasonably forecast patient health issues. The ensemble DBN framework aims to maximize the advantages of many machine learning models thereby enhancing the prediction accuracy. This allows the utilization of the complementing characteristics of these models to increase the dependability and accuracy of health status prognosis. The ensemble DBN framework and single SVM, FFNN, and NB models were compared using large-scale simulations. The prediction powers of models are evaluated using criteria including but not restricted to accuracy and f-measure. The results reveal that the ensemble DBN performs better than the single models, thereby raising accuracy and an f-measure.
References
1. Yu Z, Amin SU, Alhussein M, et al. Research on disease prediction based on improved DeepFM and IoMT. IEEE Access. 2021; 9: 39043–39054. doi: 10.1109/ACCESS.2021.3062687
2. Yuan X, Chen J, Zhang K, et al. A stable AI-based binary and multiple class heart disease prediction model for IoMT. IEEE Transactions on Industrial Informatics. 2021; 18(3): 2032–2040. doi: 10.1109/TII.2021.3098306
3. Khan MA, Algarni F. A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS. IEEE Access. 2020; 8: 122259–122269. doi: 10.1109/ACCESS.2020.3006424
4. Abbas khan T, Fatima A, Shahzad T, et al. Secure IoMT for disease prediction empowered with transfer learning in healthcare 5.0, the concept and case study. 2023. doi: 10.1109/access.2023.3266156
5. Ashfaq Z, Rafay A, Mumtaz R, et al. A review of enabling technologies for Internet of Medical Things (IoMT) Ecosystem. Ain Shams Engineering Journal. 2022; 13(4): 101660. doi: 10.1016/j.asej.2021.101660
6. Rajkumar G, Devi TG, Srinivasan A. Heart disease prediction using IoT based framework and improved deep learning approach: Medical application. Medical Engineering & Physics. 2023; 111: 103937. doi: 10.1016/j.medengphy.2022.103937
7. Awotunde J B, Folorunso S O, Ajagbe S A, et al. AiIoMT: IoMT-based system-enabled artificial intelligence for enhanced smart healthcare systems. Machine Learning for Critical Internet of Medical Things: Applications and Use Cases. 2022: 229–254. doi: 10.1007/978-3-030-80928-7_10
8. Divya K, Sirohi A, Pande S, et al. An IoMT assisted heart disease diagnostic system using machine learning techniques. Cognitive Internet of Medical Things for Smart Healthcare: Services and Applications. 2021: 145–161. doi: 10.1007/978-3-030-55833-8_9
9. Rahman A, Nasir MU, Gollapalli M, et al. IoMT‐Based mitochondrial and multifactorial genetic inheritance disorder prediction using machine learning. Computational Intelligence and Neuroscience. 2022; 2022(1): 2650742. doi: 10.1155/2022/2650742
10. Aman AHM, Hassan WH, Sameen S, et al. IoMT amid COVID-19 pandemic: Application, architecture, technology, and security. Journal of Network and Computer Applications. 2021; 174: 102886. doi: 10.1016/j.jnca.2020.102886
11. Ghantasala GP., Kumari NV, Patan, R. Cancer prediction and diagnosis hinged on HCML in IOMT environment. In Machine Learning and the Internet of Medical Things in Healthcare. 2021: 179–207. doi: 10.1016/b978-0-12-821229-5.00004-5
12. Adewole KS, Akintola AG, Jimoh RG, et al. Cloud-based IoMT framework for cardiovascular disease prediction and diagnosis in personalized E-health care. In Intelligent IoT Systems in Personalized Health Care. 2021: 105–145. doi: 10.1016/b978-0-12-821187-8.00005-8
13. Awotunde JB, Folorunso SO, Ajagbe SA, et al. AiIoMT: IoMT-based system-enabled artificial intelligence for enhanced smart healthcare systems. Machine Learning for Critical Internet of Medical Things: Applications and Use Cases. 2022: 229–254. doi: 10.1007/978-3-030-80928-7_10
14. Ghosh S, Ghosh SK. Feel: Federated learning framework for elderly healthcare using edge-IOMT. IEEE Transactions on Computational Social Systems. 2023; 10(4): 1800–1809. doi: 10.1109/TCSS.2022.3233300
15. Tripathy SS, Imoize AL, Rath M, et al. A novel edge-computing-based framework for an intelligent smart healthcare system in smart cities. Sustainability. 2022; 15(1): 735. doi: 10.3390/su15010735
16. Manimurugan S, Almutairi S, Aborokbah MM, et al. Two-stage classification model for the prediction of heart disease using IoMT and artificial intelligence. Sensors. 2022; 22(2): 476. doi: 0.3390/s22020476
17. Zhu T, Kuang L, Daniels J, et al. IoMT-enabled real-time blood glucose prediction with deep learning and edge computing. IEEE Internet of Things Journal. 2022; 10(5): 3706–3719. doi: 10.1109/JIOT.2022.3143375
18. Damayunita A, Fuadi RS, Juliane C. Comparative analysis of naive bayes, k-nearest neighbors (KNN), and support vector machine (SVM) algorithms for classification of heart disease patients. Jurnal Online Informatika. 2022; 7(2): 219–225. doi: 10.15575/join.v7i2.919
19. Yilmaz R, Yağin FH. Early detection of coronary heart disease based on machine learning methods. Medical Records. 2022; 4(1), 1–6.
20. Ningsih L, Buono A, Haryanto T. Fuzzy learning vector quantization for classification of mixed meat image based on character of color and texture. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). 2022; 6(3): 421–429.
21. Kurani A, Doshi P, Vakharia A, et al. A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Annals of Data Science. 2023; 10(1): 183–208. doi: 10.1007/s40745-021-00344-x
22. Zhong G, Li X, Song J, et al. Reconstruction of global surface ocean pCO 2 using region-specific predictors based on a stepwise FFNN regression algorithm. Biogeosciences. 2022; 19(3): 845–859. doi: 10.5194/bg-19-845-2022
23. Vu DH. Privacy-preserving Naive Bayes classification in semi-fully distributed data model. Computers & Security. 2022;115: 102630. doi: 10.1016/j.cose.2022.102630
24. Heart Disease Dataset. https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset (accessed on 10 December 2025)
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Copyright (c) 2026 Naren Thiruvalar Vellingiri, Yuvaraju Muniappan (Author)
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