Abstract
Wireless body sensor networks (WBSNs) play a vital role in monitoring the health conditions of patients and are a low-cost solution for dealing with several healthcare applications. However, processing a large amount of data and making feasible decisions in emergency cases are the major challenges attributed to WBSNs. Thus, this paper addresses these challenges by designing a deep learning approach for health risk assessment by proposing fractional cat based salp swarm algorithm (FCSSA). At first, the WBSN nodes are utilized for sensing data from patient health records to acquire certain parameters for making the assessment. Based on the obtained parameters, WBSN nodes transmit the data to the target node. Here, the hybrid harmony search algorithm and particle swarm optimization (hybrid HSA-PSO) is used for determining the optimal cluster head. Then, the results produced by the hybrid HSA-PSO are given to the target node, in which the deep belief network (DBN) is used for classifying the health records for the health risk assessment. Here, the DBN is trained using the proposed FCSSA, which is developed by integrating fractional cat swarm optimization (FCSO) and salp swarm algorithm (SSA) for initiating the classification. The proposed FCSSA-based DBN shows better performance using metrics, namely accuracy, energy, and throughput with values 94.604, 0.145, and 0.058, respectively.
Ethical Approval: The conducted research is not related to either human or animal use.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
Conflict of interests: The authors declare no conflict of interest.
References
[1] Harbouche A, Djedi N, Erradi M, Ben-Othman J, Kobbane A. Model driven flexible design of a wireless body sensor network for health monitoring. Comput Networks 2017;129:548–71.10.1016/j.comnet.2017.06.014Search in Google Scholar
[2] Shende DK, Suryavanshi N. IoT based geographic multicast routing protocol with DPA through WSN. Internat Journal Creative Research Thoughts 2018;6:578–84.Search in Google Scholar
[3] Habib C, Makhoul A, Darazi R, Couturier R. Multisensor data fusion for patient risk level determination and decision-support in wireless body sensor networks. In: Proceedings of the ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2016:221–4.10.1145/2988287.2989173Search in Google Scholar
[4] Kalaiselvi K, Suresh GR, Ravi V. Efficient shortest path approach using cluster based Warshall’s algorithmn in wireless body sensor networks. Internat Res J Eng Technol 2018;5:225–58.Search in Google Scholar
[5] Gambhir S, Kathuria M. Dynamic priority-based packet handling protocol for healthcare wireless body area network system. Internat J Computational Systems Eng 2018;4:3–16.10.1504/IJCSYSE.2018.090635Search in Google Scholar
[6] Jeong S, Youn CH, Shim EB, Kim M, Cho YM, Peng L. An integrated healthcare system for personalized chronic disease care in home–hospital environments. IEEE Trans Info Technol Biomed 2012;16:572–85.10.1109/TITB.2012.2190989Search in Google Scholar PubMed
[7] Habib C, Makhoul A, Darazi R, Salim C. Self-adaptive data collection and fusion for health monitoring based on body sensor networks. IEEE Trans Indus Inform 2016;12:2342–52.10.1109/TII.2016.2575800Search in Google Scholar
[8] Pirbhulal S, Zhang H, Mukhopadhyay SC, Li C, Wang Y, Li G, et al. An efficient biometric-based algorithm using heart rate variability for securing body sensor networks. Sensors 2015;15:15067–89.10.3390/s150715067Search in Google Scholar PubMed PubMed Central
[9] Yang J, Chen J, Su Y, Jing Q, Li Z, Yi F, et al. Eardrum-inspired active sensors for self-powered cardiovascular system characterization and throat-attached anti-interference voice recognition. Adv Mater 2015;27:1316–26.10.1002/adma.201404794Search in Google Scholar PubMed
[10] Brzeziński D. Mining data streams with concept drift. Cs Put Pozna 2010;89.Search in Google Scholar
[11] Lin Z, Chen J, Li X, Zhou Z, Meng K, Wei W, et al. Triboelectricnano generator enabled body sensor network for self-powered human heart-rate monitoring. ACS Nano 2017;11:8830–7.10.1021/acsnano.7b02975Search in Google Scholar PubMed
[12] Tambe SB, Gajre SS. Cluster-based real-time analysis of mobile healthcare application for prediction of physiological data. J Amb Intell Hum Comp 2018;9:429–45.10.1007/s12652-017-0562-9Search in Google Scholar
[13] Phadat D, Bhole A. Sensor network for patient monitoring. Int J Res Advent Technol. 2014;2:339–47.Search in Google Scholar
[14] Ganesan M, Kumar AP, Krishnan SK, Lalitha E, Manjula B, Amudhavel J. A novel based algorithm for the prediction of abnormal heart rate using Bayesian algorithm in the wireless sensor network. In Proceeding of the International Conferences on Advanced Research Computing Science and Engineering Technology, 2015:53:1–53:5.10.1145/2743065.2743118Search in Google Scholar
[15] Atallah L, Lo B, King R, Yang GZ. Sensor placement for activity detection using wearable accelerometers. In: Proceedings of the International Conferences on Body Sensor Networks, Singapore 2010:24–9.10.1109/BSN.2010.23Search in Google Scholar
[16] Kwapisz JR, Weiss GM, Moore SA. Activity recognition using cell phone accelerometers. ACM SIGKDD Explor Newslett 2010;12:74–82.10.1145/1964897.1964918Search in Google Scholar
[17] Shoaib M, Bosch S, Incel OD, Scholten H, Havinga PJM. Fusion of smartphone motion sensors for physical activity recognition. Sensors 2014;14:10146–476.10.3390/s140610146Search in Google Scholar PubMed PubMed Central
[18] Habib C, Makhoul A, Darazi R, Couturier R. Health risk assessment and decision-making for patient monitoring and decision-support using wireless body sensor networks. Inform Fusion 2019;47:10–22.10.1016/j.inffus.2018.06.008Search in Google Scholar
[19] Shih YY, Hsiu PC, Pang AC. A Data parasitizing scheme for effective health monitoring in wireless body area networks. IEEE Trans Mobile Comput. 2018;18(1):13–27.10.1109/TMC.2018.2830779Search in Google Scholar
[20] Schick L, de Souza WL, do Prado AF. Wireless body sensor network for monitoring and evaluating physical activity. In: Information Technology-New Generations, Springer, 2018:81–6.10.1007/978-3-319-54978-1_11Search in Google Scholar
[21] Aiello F, Bellifemine FL, Fortino G, Galzarano S, Gravina R. An agent-based signal processing in-node environment for real-time human activity monitoring based on wireless body sensor networks. Eng Appl Artif Intell 2011;24:1147–61.10.1016/j.engappai.2011.06.007Search in Google Scholar
[22] Fidanova S, Marinov P. Number of ants versus number of iterations on ant colony optimization algorithm for wireless sensor layout. Pencho Marinov, Published on 2013.10.1007/978-3-662-43880-0_25Search in Google Scholar
[23] Yi T-H, Li H-N, Gu M. Optimal sensor placement for structural health monitoring based on multiple optimization strategies. The Structural Design of Tall and Special Buildings 2011;20:881–900.10.1002/tal.712Search in Google Scholar
[24] Chen M, Li W, Hao Y, Qian Y, Humar I. Edge cognitive computing based smart healthcare system. Future Gener Comp Sy 2018;86:403–11.10.1016/j.future.2018.03.054Search in Google Scholar
[25] Zhang Y. Fractional-order cat swarm optimization. In: Proceedings of International Conference on Natural Computation in Fuzzy Systems and Knowledge Discovery, IEEE, Changsha, China, 2016.10.1109/FSKD.2016.7603173Search in Google Scholar
[26] Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM. Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Software. 2017;114:163–91.10.1016/j.advengsoft.2017.07.002Search in Google Scholar
[27] Heart Disease Data Set. Available from: https://archive.ics.uci.edu/ml/datasets/heart + Disease. Accessed: Jul 2018.Search in Google Scholar
[28] Shankar T, Shanmugavel S, Rajesh A. Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evolut Comput 2016;30:1–10.10.1016/j.swevo.2016.03.003Search in Google Scholar
[29] Hoang DC, Yadav P, Kumar R, Panda SK. A robust harmony search algorithm based clustering protocol for wireless sensor networks. In: Proceedings of IEEE International Conference on Communications Workshops, 2010:1–5.10.1109/ICCW.2010.5503895Search in Google Scholar
[30] Singh B, Lobiyal DK. A novel energy- aware cluster head selection based on PSO for WSN. Human Cen Comput Info Sci 2012;2:1–18.Search in Google Scholar
[31] Hinton GE, Osindero S, Teh Y. A fast learning algorithm for deep belief nets. Neural Comput 2006;18:1527–54.10.1162/neco.2006.18.7.1527Search in Google Scholar PubMed
[32] Bahrami M, Bozorg-Haddad O, Chu X. Cat swarm optimization (CSO) algorithm. In: Advanced Optimization by Nature-Inspired Algorithms, 2018:9–18.10.1007/978-981-10-5221-7_2Search in Google Scholar
[33] Gupta V, Pandey R. Modified LEACH-DT algorithm with hierarchical extension for wireless sensor networks. Int J Comp Network Info Security 2016;8:32.10.5815/ijcnis.2016.02.04Search in Google Scholar
[34] Sun Q, Wang Y, Jiang Y, Shao L, Chen D. Fault diagnosis of SEPIC converters based on PSO-DBN and wavelet packet energy spectrum. In: Proceedings of Prognostics and System Health Management, IEEE, Harbin, China, 2017.10.1109/PHM.2017.8079137Search in Google Scholar
[35] Babaoğlu I, Kıra MS, Ülker E, Gündüz M. Diagnosis of coronary artery disease using artificial bee colony and k-nearest neighbor algorithms. Internat J Comput Commun Eng 2013;2:56–9.10.7763/IJCCE.2013.V2.136Search in Google Scholar
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