In recent times, the Internet of Medical Things (IoMT) is a new loomed technology, which has been deliberated as a promising technology designed for various and broadly connected networks. In an intelligent healthcare system, the framework of IoMT observes the health circumstances of the patients dynamically and responds to backings their needs, which helps detect the symptoms of critical rare body conditions based on the data collected. Metaheuristic algorithms have proven effective, robust, and efficient in deciphering real-world optimization, clustering, forecasting, classification, and other engineering problems. The emergence of extraordinary, very large-scale data being generated from various sources such as the web, sensors, and social media has led the world to the era of big data. Big data poses a new contest to metaheuristic algorithms. So, this research work presents the metaheuristic optimization algorithm for big data analysis in the IoMT using gravitational search optimization algorithm (GSOA) and reflective belief network with convolutional neural networks (DBN-CNNs). Here the data optimization has been carried out using GSOA for the collected input data. The input data were collected for the diabetes prediction with cardiac risk prediction based on the damage in blood vessels and cardiac nerves. Collected data have been classified to predict abnormal and normal diabetes range, and based on this range, the risk for a cardiac attack has been predicted using SVM. The performance analysis is made to reveal that GSOA-DBN_CNN performs well in predicting diseases. The simulation results illustrate that the GSOA-DBN_CNN model used for prediction improves accuracy, precision, recall, F1-score, and PSNR.

City University Petaling Jaya 46100 Malaysia

Computer Science and Engineering Department University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal 462033 Madhya Pradesh India

Department of Applied Cybernetics Faculty of Science University of Hradec Kralove Hradec Kralove Czech Republic

Department of Computer Science Faculty of Engineering and Technology Liverpool John Moores University Liverpool L3 3AF UK

Department of Information Technology Dambi Dollo University Dambi Dollo Ethiopia

Department of Mathematics College of Science King Khalid University Abha Saudi Arabia

Galgotias University Greater Noida Uttar Pradesh India

School of Computing Science and Engineering VIT Bhopal University Bhopal India

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Maheswari G. U., Sujatha R., Mareeswari V., Ephzibah E. P. Machine Learning for Healthcare . Bocaraton, FL, USA: Chapman and Hall/CRC; 2020. The role of metaheuristic algorithms in healthcare.

Firdaus H., Hassan S. I., Kaur H. A comparative survey of machine learning and meta-heuristic optimization algorithms for sustainable and smart healthcare. African Journal of Comput. ICT Ref. Format . 2018;11(4):1–17.

Murugan S., Jeyalaksshmi S., Mahalakshmi B., Suseendran G., Jabeen T. N., Manikandan R. Comparison of ACO and PSO algorithm using energy consumption and load balancing in emerging MANET and VANET infrastructure. Journal of Critical Reviews . 2020;7(9)

Abugabah A., AlZubi A. A., Al-Obeidat F., Alarifi A., Alwadain A. Data mining techniques for analyzing healthcare conditions of urban space-person lung using meta-heuristic optimized neural networks. Cluster Computing . 2020;23(3):1781–1794. doi: 10.1007/s10586-020-03127-w. DOI

Saha A., Chowdhury C., Jana M., Biswas S. IoT sensor data analysis and fusion applying machine learning and meta-heuristic approaches. Enabling AI Applications in Data Science . 2021:441–469. doi: 10.1007/978-3-030-52067-0_20. DOI

Suganya P., Sumathi C. P. A novel metaheuristic data mining algorithm for the detection and classification of Parkinson disease. Indian Journal of Science and Technology . 2015;8(14):p. 1. doi: 10.17485/ijst/2015/v8i14/72685. DOI

Doulamis A., Doulamis N., Angeli A., et al. A non-invasive photonics-based device for monitoring of diabetic foot ulcers: architectural/sensorial components & technical specifications. Inventions . 2021;6(2):p. 27. doi: 10.3390/inventions6020027. DOI

Li J., Liu L. S., Fong S., et al. Adaptive swarm balancing algorithms for rare-event prediction in imbalanced healthcare data. PloS one . 2017;12(7) doi: 10.1371/journal.pone.0180830.e0180830 PubMed DOI PMC

Chatterjee S., Byun J., Dutta K., Pedersen R. U., Pottathil A., Xie H. Designing an Internet-of-Things (IoT) and sensor-based in-home monitoring system for assisting diabetes patients: iterative learning from two case studies. European Journal of Information Systems . 2018;27(6):670–685. doi: 10.1080/0960085x.2018.1485619. DOI

Fong S., Deb S., Yang X.-S., Li J. Feature selection in life science classification: metaheuristic swarm search. IT Professional . 2014;16(4):24–29. doi: 10.1109/mitp.2014.50. DOI

Hoang N. D. Image processing-based pitting corrosion detection using metaheuristic optimized multilevel image thresholding and machine-learning approaches. Mathematical Problems in Engineering . 2020;2020:19. doi: 10.1155/2020/6765274.6765274 DOI

El-Hasnony I. M., Barakat S. I., Mostafa R. R. Optimized ANFIS model using hybrid metaheuristic algorithms for Parkinson’s disease prediction in IoT e. IEEE Access . 2020;8:119252–119270. doi: 10.1109/access.2020.3005614. DOI

Mukherjee R., Diwekar U. M. Multi-objective optimization of the TEG dehydration process for BTEX emission mitigation using machine-learning and metaheuristic algorithms. ACS Sustainable Chemistry & Engineering . 2021;9(3):1213–1228. doi: 10.1021/acssuschemeng.0c06951. DOI

Singh R. Nature inspired based meta-heuristic techniques for global applications. The International Journal of Computer Applications & Information Technology . 2020;12(1):303–309.

Tayal A., Solanki A., Singh S. P. Integrated frame work for identifying sustainable manufacturing layouts based on big data, machine learning, meta-heuristic and data envelopment analysis. Sustainable Cities and Society . 2020;62 doi: 10.1016/j.scs.2020.102383.102383 DOI

Mallick J., Alqadhi S., Talukdar S., et al. Risk assessment of resources exposed to rainfall induced landslide with the development of GIS and RS based ensemble metaheuristic machine learning algorithms. Sustainability . 2021;13(2):p. 457. doi: 10.3390/su13020457. DOI

Kaul S., Kumar Y. Constraint Handling in Metaheuristics and Applications . New York, NY USA: Springer; 2021. Nature-Inspired metaheuristic algorithms for constraint handling: challenges, issues, and research perspective.

Le T. M., Vo T. M., Pham T. N., Dao S. V. T. A novel wrapper–based feature selection for early diabetes prediction enhanced with a metaheuristic. IEEE Access . 2020;9:7869–7884.

Mazaheri V., Khodadadi H. Heart arrhythmia diagnosis based on the combination of morphological, frequency and nonlinear features of ECG signals and metaheuristic feature selection algorithm. Expert Systems with Applications . 2020;161 doi: 10.1016/j.eswa.2020.113697.113697 DOI

Sampathkumar A., Murugan S., Sivaram M., Sharma V., Venkatachalam K., Kalimuthu M. Advanced energy management system for smart city application using the IoT. Internet of Things in Smart Technologies for Sustainable Urban Development . 2020:185–194. doi: 10.1007/978-3-030-34328-6_12. DOI

Manikandan V., Gowsic K., Prince T., Umamaheswari R., Ibrahim B. F., Sampathkumar A. DRCNN-IDS approach for intelligent intrusion detection system. Proceedings of the 2020 International Conference on Computing and Information Technology (ICCIT-1441); September 2020; Tabuk City, Saudi Arabia. IEEE; pp. 1–4.

Sharma A., Guleria K., Goyal N. Prediction of diabetes disease using machine learning model. Proceedings of the International Conference on Communication, Computing and Electronics Systems; October 2021; Coimbatore, India. Springer; pp. 683–692. DOI

Fazakis N., Kocsis O., Dritsas E., Alexiou S., Fakotakis N., Moustakas K. Machine learning tools for long-term type 2 diabetes risk prediction. IEEE Access . 2021;9:103737–103757. doi: 10.1109/access.2021.3098691. DOI

Abualigah L., Gandomi A. H., Elaziz M. A., et al. Advances in meta-heuristic optimization algorithms in big data text clustering. Electronics . 2021;10(2):p. 101. doi: 10.3390/electronics10020101. DOI

Sayed A. A., Abdallah M. M., Zaki A. M., Ahmed A. A. Big data analysis using a metaheuristic algorithm: twitter as case study. Proceedings of the 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE); February 2020; Aswan, Egypt. IEEE; pp. 20–26.

Zhang Y., Liu F. An improved deep belief network prediction model based on knowledge transfer. Future Internet . 2020;12(11):p. 188. doi: 10.3390/fi12110188. DOI