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Novel framework based on deep learning and cloud analytics for smart patient monitoring and recommendation (SPMR)

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Abstract

In world, patients suffering from chronic and lifestyle diseases are substantially increasing that effects social as well as economic life. In this work, initially a broad survey of ubiquitous, smart and networked healthcare systems for monitoring of patients with chronic and lifestyle diseases is presented. Afterwards, Smart Patient Monitoring and Recommendation, a novel framework based on Deep Learning (DL) and Cloud oriented analytics is proposed. Based on the patients’ vital signs and activity context, generated through Ambient Assisted Living devices, SPMR monitors and predicts the real health status and calls assistive services. The real time processing and intelligence facilitated by both Local Intelligent Processing (LIP) module and cloud oriented analytics devised in SPMR. LIP is based on predictive DL with novel Categorical Cross Entropy (CCE) Optimization. In the experimental study, imbalanced dataset collected through a case study on patients suffering from Chronic Blood Pressure disorder is utilized and real health status of patient is predicted. SPMR offers prevention and care in real time even in the absence of internet and cloud service. It eliminates the drawbacks of existing works, in which Machine Learning models and associated methods are copied to local portion. Our proposed model demonstrates the efficacy when compared with similar and recent models. The highest accuracy improvement with our model ranges from 8–18%. Also, F-score average and F-score for emergency class improved up to 17% and 36% respectively. The results show the effectiveness of SPMR even in case of emergencies.

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References

  • Abdelaziz A, Elhoseny M, Salama AS, Riad AM (2018) A machine learning model for improving healthcare services on cloud computing environment. Measurement 119:117–128

    Article  Google Scholar 

  • Alam F, Mehmood R, Katib I, Albeshri A (2016) Analysis of eight data mining algorithms for smarter Internet of Things (IoT). Procedia Comput Sci 98:437–442

    Article  Google Scholar 

  • Ara A, Ara A (2017) Case study: integrating IoT, streaming analytics and machine learning to improve intelligent diabetes management system. In: 2017 International conference on energy, communication, data analytics and soft computing (ICECDS), pp 3179–3182

  • Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54:2787–2805

    Article  MATH  Google Scholar 

  • Aziz R, Verma C, Srivastava N (2018) Artificial neural network classification of high dimensional data with novel optimization approach of dimension reduction. Ann Data Sci 5:615–635

    Article  Google Scholar 

  • Bajaj G, Motwani A (2020) Improving reliability of mobile social cloud computing using machine learning in content addressable network. Social networking and computational intelligence. Lecture notes in networks and systems. Springer, Singapore, pp 85–103. https://doi.org/10.1007/978-981-15-2071-6_8

    Book  Google Scholar 

  • Cecchinel C, Jimenez M, Mosser S, Riveill M (2014) An architecture to support the collection of big data in the internet of things. In: 2014 IEEE World congress on services, pp 442–449

  • Chen M, Li W, Hao Y, Qian Y, Humar I (2018) Edge cognitive computing based smart healthcare system. Future Gener Comput Syst 86:403–411

    Article  Google Scholar 

  • Chok NS (2010) Pearson’s versus Spearman’s and Kendall’s correlation coefficients for continuous data. University of Pittsburgh, Pittsburgh

    Google Scholar 

  • Collins GS, Omar O, Shanyinde M, Yu L-M (2013) A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods. J Clin Epidemiol 66:268–277

    Article  Google Scholar 

  • Das SK, Cook DJ (2005) Designing smart environments: a paradigm based on learning and prediction. In: International conference on pattern recognition and machine intelligence. Springer, pp 80–90

  • Echouffo-Tcheugui JB, Kengne AP (2012) Risk models to predict chronic kidney disease and its progression: a systematic review. PLOS Med 9:e1001344

    Article  Google Scholar 

  • Forkan A, Khalil I, Tari Z (2014) CoCaMAAL: a cloud-oriented context-aware middleware in ambient assisted living. Future Gener Comput Syst 35:114–127

    Article  Google Scholar 

  • Forkan ARM, Khalil I, Ibaida A, Tari Z (2015) BDCaM: Big data for context-aware monitoring—A personalized knowledge discovery framework for assisted healthcare. IEEE Trans Cloud Comput 5:628–641

    Article  Google Scholar 

  • Fortino G, Giannantonio R, Gravina R, Kuryloski P, Jafari R (2012) Enabling effective programming and flexible management of efficient body sensor network applications. IEEE Trans Hum Mach Syst 43:115–133

    Article  Google Scholar 

  • Garbhapu VV, Gopalan S (2017) IoT based low cost single sensor node remote health monitoring system. Procedia Comput Sci 113:408–415

    Article  Google Scholar 

  • González-Valenzuela S, Liang X, Cao H, Chen M, Leung VC (2012) Body area networks autonomous sensor networks. Springer, Berlin, pp 17–37

    Book  Google Scholar 

  • Gope P, Hwang T (2015) BSN-Care: a secure IoT-based modern healthcare system using body sensor network. IEEE J Biomed Health Inform 16:1368–1376

    Google Scholar 

  • Hämäläinen M, Li X (2017) Recent advances in body area network technology and applications. Int J Wirel Inf Netw 24:63–64

    Article  Google Scholar 

  • Hassan MK, El Desouky AI, Elghamrawy SM, Sarhan AM, Engineering E (2018) Intelligent hybrid remote patient-monitoring model with cloud-based framework for knowledge discovery. Comput Electr Eng 70:1034–1048

    Article  Google Scholar 

  • Hossain MS, Muhammad GJ (2016) Cloud-assisted industrial internet of things (iiot)-enabled framework for health monitoring. Comput Netw 101:192–202

    Article  Google Scholar 

  • Jauch EC et al (2010) Part 11: adult stroke: 2010 American Heart Association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation 122:S818–S828

    Article  Google Scholar 

  • Jensen D (2019) Beginning Azure IoT edge computing: extending the cloud to the intelligent edge. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4536-1

  • Keras documentation. https://keras.io/

  • Kaushar H, Ricchariya P, Motwani A (2014) Comparison of sla based energy efficient dynamic virtual machine consolidation algorithms. Int J Comput Appl 102:31–36

    Google Scholar 

  • Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International joint conference on artificial intelligence. vol 2. Montreal, Canada, pp 1137–1145

  • Libelium Comunicaciones Distribuidas S.L. (2019) MySignals SW eHealth and Medical IoT development platform technical guide. http://www.libelium.com/downloads/documentation/mysignals_technical_guide.pdf. Accessed 12 Jan 2020

  • Lont M, Milosevic D, van Roermund A (2014) Wireless body area networks wake-up receiver based ultra-low-power WBAN. Springer, Berlin, pp 7–28

    Google Scholar 

  • Malasinghe LP, Ramzan N, Dahal K (2019) Remote patient monitoring: a comprehensive study. J Ambient Intell Humaniz Comput 10:57–76

    Article  Google Scholar 

  • Motwani A, Patel V, Patil VM (2015) Power and Qos aware virtual machine consolidation in green cloud data center. Int J Electr Electron Comput Eng 4:93

    Google Scholar 

  • Muhammed T, Mehmood R, Albeshri A, Katib I (2018) UbeHealth: a personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities. IEEE Access 6:32258–32285

    Article  Google Scholar 

  • Musheer RA, Verma C, Srivastava N (2019) Novel machine learning approach for classification of high-dimensional microarray data. Soft Comput 23:13409–13421

    Article  Google Scholar 

  • Nathaniel S, Motwani A, Saxena A (2018) Cloud based predictive model for detection of ‘chronic kidney disease’ risk. Int J Comput Sci Eng 6:185–188

    Google Scholar 

  • Negra R, Jemili I, Belghith A (2016) Wireless body area networks: applications and technologies. Procedia Comput Sc 83:1274–1281

    Article  Google Scholar 

  • Nielsen MA (2015) Neural networks and deep learning, vol 25. Determination press San Francisco, CA, USA

    Google Scholar 

  • Normandeau K (2013) Beyond volume, variety and velocity is the issue of big data veracity. http://insidebigdata.com/2013/09/12/beyond-volume-variety-velocity-issue-bigdata-veracity/

  • Panagiotakopoulos TC, Lyras DP, Livaditis M, Sgarbas KN, Anastassopoulos GC, Lymberopoulos DK (2010) A contextual data mining approach toward assisting the treatment of anxiety disorders. IEEE Trans Inf Technol Biomed 14:567–581

    Article  Google Scholar 

  • Rathore MM, Ahmad A, Paul A, Wan J, Zhang D (2016) Real-time medical emergency response system: exploiting IoT and big data for public health. J Med Syst 40:283

    Article  Google Scholar 

  • Roderick O, Marko N, Sanchez D, Aryasomajula A, Handbook DA (2017) Chapter 18—Data analysis and machine learning effort in healthcare: organization, limitations, and development of an approach. In: Geng H (ed) Internet of things and data analytics handbook. Wiley, pp 295–328. https://doi.org/10.1002/9781119173601.ch18

  • Saeed M et al (2011) Multiparameter intelligent monitoring in intensive care II (MIMIC-II): a public-access intensive care unit database. Crit Care Med 3(39):952

    Article  Google Scholar 

  • Sarker VK, Jiang M, Gia TN, Anzanpour A, Rahmani AM, Liljeberg P (2017) Portable multipurpose bio-signal acquisition and wireless streaming device for wearables. In: 2017 IEEE sensors applications symposium (SAS), pp 1–6

  • Schriger DL (2012) Approach to the patient with abnormal vital signs. Goldman’s Cecil Medicine. Elsevier, Amsterdam, pp 27–30

    Google Scholar 

  • Sebastian S, Ray (2015) Development of IoT invasive architecture for complying with health of home. In: Proceedings of I3CS, Shillong, pp 79–83

  • Sohraby K, Minoli D, Znati T (2007) Wireless sensor networks: technology, protocols, and applications. John Wiley & Sons, New York

    Book  Google Scholar 

  • Stüber GL (2017) Principles of mobile communication. Springer, Berlin

    Book  Google Scholar 

  • Sun G, Chang V, Yang G, Liao D (2018) The cost-efficient deployment of replica servers in virtual content distribution networks for data fusion. Inf Sci 432:495–515

    Article  MathSciNet  Google Scholar 

  • Wang S-H, Govindaraj VV, Górriz JM, Zhang X, Zhang Y-D (2020a) Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Inf Fusion 67:208–229

    Article  Google Scholar 

  • Wang S-H, Zhang Y-D (2020b) DenseNet-201 based deep neural network with composite learning factor and precomputation for multiple sclerosis classification. ACM Trans Multimed Comput Commun Appl 16:1–19

    Google Scholar 

  • World Health Organization (2019a) Global health estimates 2016: disease burden by cause, age, sex, by country and by region, 2000–2016. Geneva, 2018

  • World Health Organization (2019b) World health statistics 2019: monitoring health for the SDGs, sustainable development goals

  • Young S, Abdou T, Bener A (2018) Deep super learner: a deep ensemble for classification problems. In: Canadian conference on artificial intelligence. Springer, pp 84–95

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  1. University Institute of Technology, RGPV, Bhopal, 462023, Madhya Pradesh, India

    Anand Motwani, Piyush Kumar Shukla & Mahesh Pawar

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  1. Anand Motwani
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  2. Piyush Kumar Shukla
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  3. Mahesh Pawar
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Correspondence to Anand Motwani.

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Motwani, A., Shukla, P.K. & Pawar, M. Novel framework based on deep learning and cloud analytics for smart patient monitoring and recommendation (SPMR). J Ambient Intell Human Comput 14, 5565–5580 (2023). https://doi.org/10.1007/s12652-020-02790-6

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