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Mobile-Based Patient Monitoring Systems: A Prioritisation Framework Using Multi-Criteria Decision-Making Techniques

  • E. M. Almahdi
  • A. A. ZaidanEmail author
  • B. B. Zaidan
  • M. A. Alsalem
  • O. S. Albahri
  • A. S. Albahri
Systems-Level Quality Improvement
  • 16 Downloads
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

This study presents a prioritisation framework for mobile patient monitoring systems (MPMSs) based on multicriteria analysis in architectural components. This framework selects the most appropriate system amongst available MPMSs for the telemedicine environment. Prioritisation of MPMSs is a challenging task due to (a) multiple evaluation criteria, (b) importance of criteria, (c) data variation and (d) unmeasurable values. The secondary data presented as the decision evaluation matrix include six systems (namely, Yale–National Aeronautics and Space Administration (NASA), advanced health and disaster aid network, personalised health monitoring, CMS, MobiHealth and NTU) as alternatives and 13 criteria (namely, supported number of sensors, sensor front-end (SFE) communication, SFE to mobile base unit (MBU) communications, display of biosignals on the MBU, storage of biosignals on the MBU, intra-body area network (BAN) communication problems, extra-BAN communication problems, extra-BAN communication technology, extra-BAN communication protocols, back-end system communication technology, intended geographic area of use, end-to-end security and reported trial problems) based on the architectural components of MPMSs. These criteria are adopted from the most relevant studies and are found to be applicable to this study. The prioritisation framework is developed in three stages. (1) The unmeasurable values of the MPMS evaluation criteria in the adopted decision evaluation matrix based on expert opinion are represented by using the best–worst method (BWM). (2) The importance of the evaluation criteria based on the architectural components of the MPMS is determined by using the BWM. (3) The VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is utilised to rank the MPMSs according to the determined importance of the evaluation criteria and the adopted decision matrix. For validation, mean ± standard deviation is used to verify the similarity of systematic prioritisations objectively. The following results are obtained. (1) The BWM represents the unmeasurable values of the MPMS evaluation criteria. (2) The BWM is suitable for weighing the evaluation criteria based on the architectural components of the MPMS. (3) VIKOR is suitable for solving the MPMS prioritisation problem. Moreover, the internal and external VIKOR group decision making are approximately the same, with the best MPMS being ‘Yale–NASA’ and the worst MPMS being ‘NTU’. (4) For the objective validation, remarkable differences are observed between the group scores, which indicate the similarity of internal and external prioritisation results.

Keywords

Mobile patient monitoring system Multicriteria decision-making technique VIKOR BWM 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

10916_2019_1339_MOESM1_ESM.docx (182 kb)
ESM 1 (DOCX 182 kb)

References

  1. 1.
    Iqbal, S. et al., Real-time-based E-health systems: design and implementation of a lightweight key management protocol for securing sensitive information of patients. Health Technol. (Berl).:1–19, 2018.Google Scholar
  2. 2.
    Alanazi, H. O. et al., Meeting the Security Requirements of Electronic Medical Records in the ERA of High-Speed Computing. J. Med. Syst. 39(1):165, 2015.CrossRefGoogle Scholar
  3. 3.
    Nabi, M. S. A. et al., Suitability of Using SOAP Protocol to Secure Electronic Medical Record Databases Transmission. Int. J. Pharmacol. 6(6):959–964, 2010.CrossRefGoogle Scholar
  4. 4.
    Kiah, M. L. M. et al., An Enhanced Security Solution for Electronic Medical Records Based on AES Hybrid Technique with SOAP/XML and SHA-1. J. Med. Syst. 37(5):9971, 2013.CrossRefGoogle Scholar
  5. 5.
    Nabi, M. S., et al., Suitability of adopting S/MIME and OpenPGP email messages protocol to secure electronic medical records. In: Second International Conference on Future Generation Communication Technologies (FGCT 2013), pp. 93–97, 2013.Google Scholar
  6. 6.
    Kiah, M. L. M. et al., Open source EMR software: Profiling, insights and hands-on analysis. Comput. Methods Prog. Biomed. 117(2):360–382, 2014.CrossRefGoogle Scholar
  7. 7.
    Zaidan, B. B. et al., A Security Framework for Nationwide Health Information Exchange based on Telehealth Strategy. J. Med. Syst. 39(5):51, 2015.CrossRefGoogle Scholar
  8. 8.
    Zaidan, B. B. et al., Impact of data privacy and confidentiality on developing telemedicine applications: A review participates opinion and expert concerns. Int. J. Pharmacol. 7(3):382–387, 2011.CrossRefGoogle Scholar
  9. 9.
    Kiah, M. L. M. et al., MIRASS: Medical Informatics Research Activity Support System Using Information Mashup Network. J. Med. Syst. 38(4):37, 2014.CrossRefGoogle Scholar
  10. 10.
    Mohsin, A. H. et al., Based Blockchain-PSO-AES Techniques in Finger Vein Biometrics: A novel Verification Secure Framework for Patient Authentication. Comput. Stand. Interfaces, 2019.Google Scholar
  11. 11.
    Mohsin, A. H. et al., Blockchain authentication of network applications: Taxonomy, classification, capabilities, open challenges, motivations, recommendations and future directions. Comput. Stand. Interfaces, 2018.Google Scholar
  12. 12.
    Mohsin, A. H. et al., Based medical systems for patient’s authentication: Towards a new verification secure framework using CIA standard. J. Med. Syst., 2019.Google Scholar
  13. 13.
    Mohsin, A. H. et al., Real-Time Medical Systems Based on Human Biometric Steganography: a Systematic Review. J. Med. Syst. 42(12):245, 2018.CrossRefGoogle Scholar
  14. 14.
    Mohsin, A. H. et al., Real-Time Remote Health Monitoring Systems Using Body Sensor Information and Finger Vein Biometric Verification: A Multi-Layer Systematic Review. J. Med. Syst. 42(12):238, 2018.CrossRefGoogle Scholar
  15. 15.
    Albahri, O. S. et al., Systematic Review of Real-time Remote Health Monitoring System in Triage and Priority-Based Sensor Technology: Taxonomy, Open Challenges, Motivation and Recommendations. J. Med. Syst. 42(5), 2018.Google Scholar
  16. 16.
    Abdulnabi, M. et al., A distributed framework for health information exchange using smartphone technologies. J. Biomed. Inform. 69:230–250, 2017.CrossRefGoogle Scholar
  17. 17.
    Salman, O. H. et al., Novel Methodology for Triage and Prioritizing Using ‘Big Data’ Patients with Chronic Heart Diseases Through Telemedicine Environmental. Int. J. Inf. Technol. Decis. Mak. 16(05):1211–1245, 2017.CrossRefGoogle Scholar
  18. 18.
    Zaidan, A. A. et al., Challenges, Alternatives, and Paths to Sustainability: Better Public Health Promotion Using Social Networking Pages as Key Tools. J. Med. Syst. 39(2):7, 2015.CrossRefGoogle Scholar
  19. 19.
    Mat Kiah, M. L. et al., Design and Develop a Video Conferencing Framework for Real-Time Telemedicine Applications Using Secure Group-Based Communication Architecture. J. Med. Syst. 38(10):133, 2014.CrossRefGoogle Scholar
  20. 20.
    Kalid, N. et al., Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization ‘Large Scales Data’ Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology. J. Med. Syst. 42(4), 2018.Google Scholar
  21. 21.
    Shuwandy, M. L. et al., Sensor-Based mHealth Authentication for Real-Time Remote Healthcare Monitoring System: A Multilayer Systematic Review. J. Med. Syst. 43(2):33, 2019.CrossRefGoogle Scholar
  22. 22.
    Talal, M. et al., Smart Home-based IoT for Real-time and Secure Remote Health Monitoring of Triage and Priority System using Body Sensors: Multi-driven Systematic Review. J. Med. Syst. 43(3):42, 2019.CrossRefGoogle Scholar
  23. 23.
    Hussain, M. et al., The landscape of research on smartphone medical apps: Coherent taxonomy, motivations, open challenges and recommendations. Comput. Methods Prog. Biomed. 122(3):393–408, 2015.CrossRefGoogle Scholar
  24. 24.
    Hussain, M. et al., Conceptual framework for the security of mobile health applications on Android platform. Telemat. Informatics 35(5), 2018.CrossRefGoogle Scholar
  25. 25.
    Hussain, M. et al., A security framework for mHealth apps on Android platform. Comput. Secur. 75:191–217, 2018.CrossRefGoogle Scholar
  26. 26.
    Cameron, J. D., Ramaprasad, A., and Syn, T., An ontology of and roadmap for mHealth research. Int. J. Med. Inform. 100:16–25, 2017.CrossRefGoogle Scholar
  27. 27.
    Pawar, P. A., and Mohammad, S. P., Review of quality of service in the mobile patient monitoring systems. In: 2017 IEEE Region 10 Symposium (TENSYMP), pp. 1-6, 2017.Google Scholar
  28. 28.
    De la Oliva, A., Bernardos, C. J., Calderon, M., Melia, T., and Zuniga, J. C., IP flow mobility: smart traffic offload for future wireless networks. IEEE Commun. Mag. 49, 2011.Google Scholar
  29. 29.
    Varga, N., Bokor, L., and Takács, A., Context-aware IPv6 Flow Mobility for Multi-sensor Based Mobile Patient Monitoring and Tele-consultation. Procedia Computer Science 40:222–229, 2014.CrossRefGoogle Scholar
  30. 30.
    Villarreal, V., Urzaiz, G., Hervas, R., and Bravo, J., Monitoring architecture to collect measurement data and medical patient control through mobile devices, 2011.Google Scholar
  31. 31.
    Ren, Y., Werner, R., Pazzi, N., and Boukerche, A., Monitoring patients via a secure and mobile healthcare system. IEEE Wirel. Commun. 17, 2010.CrossRefGoogle Scholar
  32. 32.
    Pawar, P., Jones, V., Van Beijnum, B.-J. F., and Hermens, H., A framework for the comparison of mobile patient monitoring systems. J. Biomed. Inform. 45:544–556, 2012.CrossRefGoogle Scholar
  33. 33.
    Jones, V., Gay, V., and Leijdekkers, P., Body sensor networks for mobile health monitoring: Experience in europe and australia. In: Digital Society, 2010. ICDS'10. Fourth International Conference on, pp. 204-209, 2010.Google Scholar
  34. 34.
    Hussain, A., Wenbi, R., da Silva, A. L., Nadher, M., and Mudhish, M., Health and emergency-care platform for the elderly and disabled people in the Smart City. J. Syst. Softw. 110:253–263, 2015.CrossRefGoogle Scholar
  35. 35.
    Martínez-Alcalá, C. I., Muñoz, M., and Monguet-Fierro, J., Design and customization of telemedicine systems. Computational and Mathematical Methods in Medicine 2013, 2013.CrossRefGoogle Scholar
  36. 36.
    Paliwal, G., and Kiwelekar, A. W., A comparison of mobile patient monitoring systems. International Conference on Health Information Science:198–209, 2013.Google Scholar
  37. 37.
    Khatari, M. et al., Multi-Criteria Evaluation and Benchmarking for Active Queue Management Methods: Open Issues, Challenges and Recommended Pathway Solutions. Int. J. Inf. Technol. Decis. Mak.:S0219622019300039, 2019.Google Scholar
  38. 38.
    Zaidan, A. A. et al., Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. J. Biomed. Inform. 53:390–404, 2015.CrossRefGoogle Scholar
  39. 39.
    Zaidan, A. A. et al., Multi-criteria analysis for OS-EMR software selection problem: A comparative study. Decis. Support. Syst. 78:15–27, 2015.CrossRefGoogle Scholar
  40. 40.
    Zaidan, B. B. et al., A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi-criteria analysis based on ‘large-scale data. Softw. Pract. Exp. 47(10):1365–1392, 2017.CrossRefGoogle Scholar
  41. 41.
    Yas, Q. M. et al., Towards on develop a framework for the evaluation and benchmarking of skin detectors based on artificial intelligent models using multi-criteria decision-making techniques. Int. J. Pattern Recognit. Artif. Intell. 31(03):1759002, 2017.CrossRefGoogle Scholar
  42. 42.
    Abdullateef, B. N. et al., An evaluation and selection problems of OSS-LMS packages. Springerplus 5(1):248, 2016.CrossRefGoogle Scholar
  43. 43.
    Keeney, R. L., and Raiffa, H., Decisions with multiple objectives: preferences and value trade-offs. Cambridge: Cambridge University Press, 1993.CrossRefGoogle Scholar
  44. 44.
    Zaidan, B. B. et al., A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques. Int. J. Inf. Technol. Decis. Mak.:1–42, 2017.Google Scholar
  45. 45.
    Zaidan, B. B., and Zaidan, A. A., Software and hardware FPGA-based digital watermarking and steganography approaches: Toward new methodology for evaluation and benchmarking using multi-criteria decision-making techniques. J. Circuits, Syst. Comput. 26(07):1750116, 2017.CrossRefGoogle Scholar
  46. 46.
    Rahmatullah, B., et al., Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection. In: 2017 4th International Conference on Control, Decision and Information Technologies. CoDIT 2017, 2017, pp. 1084–1088, 2017.Google Scholar
  47. 47.
    Jumaah, F. M. et al., Technique for order performance by similarity to ideal solution for solving complex situations in multi-criteria optimization of the tracking channels of GPS baseband telecommunication receivers. Telecommun. Syst.:1–19, 2017.Google Scholar
  48. 48.
    Qader, M. A. et al., A methodology for football players selection problem based on multi-measurements criteria analysis. Meas. J. Int. Meas. Confed. 111:38–50, 2017.CrossRefGoogle Scholar
  49. 49.
    Yas, Q. M. et al., Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions. Measurement 114:243–260, 2018.CrossRefGoogle Scholar
  50. 50.
    Malczewski, J., GIS and multicriteria decision analysis. Hoboken: John Wiley & Sons, 1999.Google Scholar
  51. 51.
    Petrovic-Lazarevic, S., and Abraham, A., Hybrid fuzzy-linear programming approach for multi criteria decision making problems. arXiv preprint cs/0405019, 2004.Google Scholar
  52. 52.
    Zaidan, B. B., and Zaidan, A. A., Comparative study on the evaluation and benchmarking information hiding approaches based multi-measurement analysis using TOPSIS method with different normalisation, separation and context techniques. Measurement 117:277–294, 2018.CrossRefGoogle Scholar
  53. 53.
    Zaidan, A. A. et al., A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution. Health Technol. (Berl). 8(4):223–238, 2018.CrossRefGoogle Scholar
  54. 54.
    Alsalem, M. A. et al., Systematic Review of an Automated Multiclass Detection and Classification System for Acute Leukaemia in Terms of Evaluation and Benchmarking, Open Challenges, Issues and Methodological Aspects. J. Med. Syst. 42(11):204, 2018.CrossRefGoogle Scholar
  55. 55.
    Tariq, I. et al., MOGSABAT: a metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Comput. & Applic. 30:1–15, 2018.Google Scholar
  56. 56.
    Enaizan, O. et al., Electronic medical record systems: decision support examination framework for individual, security and privacy concerns using multi-perspective analysis. Health Technol. (Berl)., 2018.Google Scholar
  57. 57.
    Salih, M. M. et al., Survey on Fuzzy TOPSIS State-of-the-Art between 2007–2017. Comput. Oper. Res., 2018.Google Scholar
  58. 58.
    Kalid, N. et al., Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related" Big Data" Using Body Sensors information and Communication Technology. J. Med. Syst. 42(2):30, 2018.CrossRefGoogle Scholar
  59. 59.
    Zionts, S., MCDM—if not a roman numeral, then what? Interfaces 9:94–101, 1979.CrossRefGoogle Scholar
  60. 60.
    Jumaah, F. M. et al., Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment. Measurement 118:83–95, 2018.CrossRefGoogle Scholar
  61. 61.
    Albahri, A. S. et al., Real-Time Fault-Tolerant mHealth System: Comprehensive Review of Healthcare Services, Opens Issues, Challenges and Methodological Aspects. J. Med. Syst. 42(8. Springer US):137, 2018.CrossRefGoogle Scholar
  62. 62.
    Albahri, O. S. et al., Real-Time Remote Health-Monitoring Systems in a Medical Centre: A Review of the Provision of Healthcare Services-Based Body Sensor Information, Open Challenges and Methodological Aspects. J. Med. Syst. 42(9):164, 2018.CrossRefGoogle Scholar
  63. 63.
    Talal, M. et al., Comprehensive Review and Analysis of Anti-Malware Apps for Smartphones. Telecommun. Syst., 2019.Google Scholar
  64. 64.
    Oliveira, M., Fontes, D. B., and Pereira, T., Multicriteria decision making: a case study in the automobile industry, 2013.Google Scholar
  65. 65.
    Jadhav, A., and Sonar, R., Analytic hierarchy process (AHP), weighted scoring method (WSM), and hybrid knowledge based system (HKBS) for software selection: a comparative study. In: Emerging trends in engineering and technology (ICETET), 2009 2nd international conference on, pp. 991-997, 2009.Google Scholar
  66. 66.
    Zaidan, A. A. et al., Based Multi-Agent learning Neural Network and Bayesian for Real-Time IoT Skin Detectors: A new Evaluation and Benchmarking Methodology. Neural Comput. & Applic., 2019.Google Scholar
  67. 67.
    Albahri, A. S. et al., Based Multiple Heterogeneous Wearable Sensors: A Smart Real-Time Health Monitoring Structured for Hospitals Distributor. IEEE Access 7:37269–37323, 2019.CrossRefGoogle Scholar
  68. 68.
    Albahri, O. S. et al., Fault-Tolerant mHealth Framework in the Context of IoT-Based Real-Time Wearable Health Data Sensors. IEEE Access 7:50052–50080, 2019.CrossRefGoogle Scholar
  69. 69.
    Mühlbacher, A. C., and Kaczynski, A., Making good decisions in healthcare with multi-criteria decision analysis: the use, current research and future development of MCDA. Applied Health Economics and Health Policy 14:29–40, 2016.CrossRefGoogle Scholar
  70. 70.
    Abdullateef, B. N., Elias, N. F., Mohamed, H., Zaidan, A., and Zaidan, B., An evaluation and selection problems of OSS-LMS packages. SpringerPlus 5:248, 2016.CrossRefGoogle Scholar
  71. 71.
    Adunlin, G., Diaby, V., and Xiao, H., Application of multicriteria decision analysis in health care: a systematic review and bibliometric analysis. Health Expect. 18:1894–1905, 2015.CrossRefGoogle Scholar
  72. 72.
    Zhu, G.-N., Hu, J., Qi, J., Gu, C.-C., and Peng, Y.-H., An integrated AHP and VIKOR for design concept evaluation based on rough number. Adv. Eng. Inform. 29:408–418, 2015.CrossRefGoogle Scholar
  73. 73.
    Raviv, G., Shapira, A., and Fishbain, B., AHP-based analysis of the risk potential of safety incidents: Case study of cranes in the construction industry. Saf. Sci. 91:298–309, 2017.CrossRefGoogle Scholar
  74. 74.
    Zhao, H., Guo, S., and Zhao, H., Comprehensive benefit evaluation of eco-industrial parks by employing the best-worst method based on circular economy and sustainability. Environ. Dev. Sustain. 20:1229–1253, 2018.CrossRefGoogle Scholar
  75. 75.
    Chou, S.-Y., Chang, Y.-H., and Shen, C.-Y., A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. Eur. J. Oper. Res. 189:132–145, 2008.CrossRefGoogle Scholar
  76. 76.
    Singh, A., and Malik, S. K., Major MCDM Techniques and their application-A Review. IOSR Journal of Engineering (IOSRJEN), ISSN (e): 2250-3021 4:2278–8719, 2014.ISSNGoogle Scholar
  77. 77.
    Jablonsky, J., MS Excel based software support tools for decision problems with multiple criteria. Procedia Economics and Finance 12:251–258, 2014.CrossRefGoogle Scholar
  78. 78.
    Ahmad, W. N. K. W., Rezaei, J., Sadaghiani, S., and Tavasszy, L. A., Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method. J. Clean. Prod. 153:242–252, 2017.CrossRefGoogle Scholar
  79. 79.
    Gupta, H., and Barua, M. K., Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. J. Clean. Prod. 152:242–258, 2017.CrossRefGoogle Scholar
  80. 80.
    Rezaei, J., Best-worst multi-criteria decision-making method. Omega 53:49–57, 2015.CrossRefGoogle Scholar
  81. 81.
    Rezaei, J., Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega 64:126–130, 2016.CrossRefGoogle Scholar
  82. 82.
    Gupta, H., Evaluating service quality of airline industry using hybrid best worst method and VIKOR. J. Air Transp. Manag. 68:35–47, 2018.CrossRefGoogle Scholar
  83. 83.
    Opricovic, S., and Tzeng, G.-H., Extended VIKOR method in comparison with outranking methods. Eur. J. Oper. Res. 178:514–529, 2007.CrossRefGoogle Scholar
  84. 84.
    Opricovic, S., and Tzeng, G.-H., Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156:445–455, 2004.CrossRefGoogle Scholar
  85. 85.
    Mahjouri, M., Ishak, M. B., Torabian, A., Manaf, L. A., Halimoon, N., and Ghoddusi, J., Optimal selection of Iron and Steel wastewater treatment technology using integrated multi-criteria decision-making techniques and fuzzy logic. Process Saf. Environ. Prot. 107:54–68, 2017.CrossRefGoogle Scholar
  86. 86.
    Diaby, V., Campbell, K., and Goeree, R., Multi-criteria decision analysis (MCDA) in health care: a bibliometric analysis. Operations Research for Health Care 2:20–24, 2013.CrossRefGoogle Scholar
  87. 87.
    Tian, Z.-p., Wang, J.-q., and Zhang, H.-y., An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Appl. Soft Comput., 2018.Google Scholar
  88. 88.
    Ren, J., Selection of sustainable prime mover for combined cooling, heat, and power technologies under uncertainties: An interval multicriteria decision-making approach. Int. J. Energy Res., 2018.Google Scholar
  89. 89.
    Serrai, W., Abdelli, A., Mokdad, L., and Hammal, Y., An efficient approach for Web service selection. In: Computers and Communication (ISCC), 2016 IEEE Symposium on, pp. 167-172, 2016.Google Scholar
  90. 90.
    Shojaei, P., Haeri, S. A. S., and Mohammadi, S., Airports evaluation and ranking model using Taguchi loss function, best-worst method and VIKOR technique. J. Air Transp. Manag. 68:4–13, 2018.CrossRefGoogle Scholar
  91. 91.
    Serrai, W., Abdelli, A., Mokdad, L., and Hammal, Y., Towards an efficient and a more accurate web service selection using MCDM methods. J. Comput. Sci. 22:253–267, 2017.CrossRefGoogle Scholar
  92. 92.
    Pamučar, D., Petrović, I., and Ćirović, G., Modification of the Best–Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers. Expert Syst. Appl. 91:89–106, 2018.CrossRefGoogle Scholar
  93. 93.
    Migdadi, M., Knowledge management enablers and outcomes in the small-and-medium sized enterprises. Ind. Manag. Data Syst. 109:840–858, 2009.CrossRefGoogle Scholar
  94. 94.
    Zaidan, A., Zaidan, B., Al-Haiqi, A., Kiah, M. L. M., Hussain, M., and Abdulnabi, M., Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. J. Biomed. Inform. 53:390–404, 2015.CrossRefGoogle Scholar
  95. 95.
    Zaidan, A., Zaidan, B., Hussain, M., Haiqi, A., Kiah, M. M., and Abdulnabi, M., Multi-criteria analysis for OS-EMR software selection problem: A comparative study. Decis. Support. Syst. 78:15–27, 2015.CrossRefGoogle Scholar
  96. 96.
    Kiah, M. L. M., Haiqi, A., Zaidan, B., and Zaidan, A., Open source EMR software: profiling, insights and hands-on analysis. Comput. Methods Prog. Biomed. 117:360–382, 2014.CrossRefGoogle Scholar
  97. 97.
    de Paiva Guimarães, M., and Martins, V. F., A checklist to evaluate Augmented Reality Applications. In: 2014 XVI Symposium on Virtual and Augmented Reality (SVR), pp. 45-52, 2014.Google Scholar
  98. 98.
    Huang, P. H., and Moh, T.-t., A non-linear non-weight method for multi-criteria decision making. Ann. Oper. Res. 248:239–251, 2017.CrossRefGoogle Scholar
  99. 99.
    Kalid, N., Zaidan, A. A., Zaidan, B. B., Salman, O. H., Hashim, M., Albahri, O. S. et al., Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization “Large Scales Data” Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology. J. Med. Syst. 42:69, March 02 2018.CrossRefGoogle Scholar
  100. 100.
    Qader, M. A., Zaidan, B. B., Zaidan, A. A., Ali, S. K., Kamaluddin, M. A., and Radzi, W. B., A methodology for football players selection problem based on multi-measurements criteria analysis. Measurement 111:38–50, 2017.CrossRefGoogle Scholar
  101. 101.
    Jones, V., Van Halteren, A., Dokovsky, N., Koprinkov, G., Bults, R., Konstantas, D., et al., Mobihealth: Mobile health services based on body area networks. In: M-Health, ed: Springer, pp. 219-236, 2006.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • E. M. Almahdi
    • 1
  • A. A. Zaidan
    • 1
    Email author
  • B. B. Zaidan
    • 1
  • M. A. Alsalem
    • 1
    • 2
  • O. S. Albahri
    • 1
  • A. S. Albahri
    • 1
    • 3
  1. 1.Department of ComputingUniversiti Pendidikan Sultan IdrisTanjong MalimMalaysia
  2. 2.Department of Management Information System, College of Administration and EconomicUniversity of MosulMosulIraq
  3. 3.College of EngineeringUniversity of Information Technology and CommunicationsBaghdadIraq

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