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Development of IoT-based mhealth framework for various cases of heart disease patients

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Abstract

A newly distributed fault-tolerant mHealth framework-based Internet of things (IoT) is proposed in this study to resolve the essential problems of healthcare service provision during the occurrence of frequent failures in a telemedicine architecture. Two models are presented to support the telehealth development of chronic heart disease (CHD) in a distant environment. In model-1, a new local multisensor fusion triage algorithm known as three-level localisation triage (3LLT) is proposed. In 3LLT, a group of heterogeneous sources is applied to triage patients as a clinical process, and the emergency levels inside/outside the home of a patient with CHD are determined. Failures related to sensor fusion can also be detected. In model-2, a centralised IoT connection towards distributed smart hospitals is employed by mHealth based on two attributes: (1) healthcare service packages and (2) time of arrival of a patient at a hospital. Three decision matrices have been used to overcome several issues on hospital selection based on multi-criteria decision-making by using an analytic hierarchy process. Two datasets are utilised: (1) a clinical CHD dataset, which includes 572 patients for testing model-1, and (2) a nonclinical dataset, which includes hospital healthcare service packages for testing model-2. Consequently, patients with CHD can be triaged into different emergency levels (risk, urgent and sick) with mHealth, and a final decision is made by selecting an appropriate hospital. Results are obtained through the clinical triage of patients, and different scenarios are provided for hospital selection. The verification of statistical results indicates that the proposed mHealth framework is systematically valid. The contribution of the mHealth framework is presented to provide an improved triage process, afford timely services and treatment for CVD patients and minimise the chances of error. These health sectors and policymakers can also recognise the evaluation benefits of smart hospitals by using the presented framework and move forward to fully automated mHealth applications.

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References

  1. Shojanoori R, Juric R. Semantic remote patient monitoring system. Telemed e-Health. 2013;19(2):129–36.

    Article  Google Scholar 

  2. Albahri OS, Albahri AS, Mohammed KI, Zaidan AA, Zaidan BB, Hashim M, 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. 2018;42(5):80. Available from: http://link.springer.com/10.1007/s10916-018-0943-4.

    Article  Google Scholar 

  3. Alanazi HO, Mat Kiah M, Zaidan A, Zaidan B, Alam GM. Secure topology for electronic medical record transmissions. Int J Pharmacol. 2010;6(6):954–8.

    Article  Google Scholar 

  4. Nabi MSA, Kiah MM, Zaidan B, Zaidan A, Alam GM. Suitability of using SOAP protocol to secure electronic medical record databases transmission. Int J Pharmacol. 2010;6(6):959–64.

    Article  Google Scholar 

  5. Nabi M, Kiah MM, Zaidan B, Zaidan A, Alam G. Suitability of SOAP protocol in securing transmissions of EMR database. Int J Pharmacol. 2010;6(6):959–64.

    Article  Google Scholar 

  6. Alanazi HO, Alam GM, Zaidan B, Zaidan A. Securing electronic medical records transmissions over unsecured communications: An overview for better medical governance. J Med Plant Res. 2010;4(19):2059–74.

    Article  Google Scholar 

  7. Zaidan B, Zaidan A, Mat KM. Impact of data privacy and confidentiality on developing telemedicine applications: A review participates opinion and expert concerns. Int J Pharmacol. 2011;7(3):382–7.

    Article  Google Scholar 

  8. Kiah MM, Nabi MS, Zaidan B, Zaidan A. An enhanced security solution for electronic medical records based on AES hybrid technique with SOAP/XML and SHA-1. J Med Syst. 2013;37(5):1–18.

    Article  Google Scholar 

  9. Nabi MS, Kiah MM, Zaidan A, Zaidan B, editors. Suitability of adopting S/MIME and OpenPGP email messages protocol to secure electronic medical records. Second international conference on future generation communication technologies (FGCT 2013); 2013: IEEE.

  10. Kiah MLM, Zaidan B, Zaidan A, Nabi M, Ibraheem R. MIRASS: Medical informatics research activity support system using information mashup network. J Med Syst. 2014;38(4):1–15.

    Article  Google Scholar 

  11. Gagnon MP, Duplantie J, Fortin JP, Lamothe L, Légaré F, Labrecque M. Integrating scientific evidence to support telehomecare development in a remote region. Telemed e-Health. 2009;15(2):195–8.

    Article  Google Scholar 

  12. Yahyaie M, Tarokh MJ, Mahmoodyar MA. Use of Internet of Things to Provide a New Model for Remote Heart Attack Prediction. Telemed e-Health. 2019;25(6):499–510.

    Article  Google Scholar 

  13. Mohammed KI, Jaafar J, Zaidan AA, Albahri OS, Zaidan BB, Abdulkareem KH, et al. A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated from Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method. IEEE Access. 2020;8:91521–30.

    Article  Google Scholar 

  14. Kiah MLM, Haiqi A, Zaidan B, Zaidan A. Open source EMR software: Profiling, insights and hands-on analysis. Comput Methods Programs Biomed. 2014;117(2):360–82.

    Article  Google Scholar 

  15. Kiah MM, Al-Bakri S, Zaidan A, Zaidan B, Hussain M. Design and develop a video conferencing framework for real-time telemedicine applications using secure group-based communication architecture. J Med Syst. 2014;38(10):1–11.

    Google Scholar 

  16. Alanazi HO, Zaidan A, Zaidan B, Kiah MM, Al-Bakri S. Meeting the security requirements of electronic medical records in the ERA of high-speed computing. J Med Syst. 2015;39(1):1–13.

    Article  Google Scholar 

  17. Zaidan B, Haiqi A, Zaidan A, Abdulnabi M, Kiah MM, Muzamel H. A security framework for nationwide health information exchange based on telehealth strategy. J Med Syst. 2015;39(5):1–19.

    Article  Google Scholar 

  18. Zaidan A, Zaidan B, Kadhem Z, Larbani M, Lakulu M, Hashim M. Challenges, alternatives, and paths to sustainability: Better public health promotion using social networking pages as key tools. J Med Syst. 2015;39(2):1–14.

    Article  Google Scholar 

  19. Hussain M, Al-Haiqi A, Zaidan A, Zaidan B, Kiah MLM, Anuar NB, et al. The landscape of research on smartphone medical apps: Coherent taxonomy, motivations, open challenges and recommendations. Comput Methods Programs Biomed. 2015;122(3):393–408.

    Article  Google Scholar 

  20. Hussain M, Al-Haiqi A, Zaidan AA, Zaidan BB, Kiah M, Iqbal S, et al. A security framework for mHealth apps on Android platform. Comput Secur. 2018;75:191–217.

    Article  Google Scholar 

  21. Yas QM, Zaidan A, Zaidan B, Hashim M, Lim CK. A systematic review on smartphone skin cancer apps: coherent taxonomy, motivations, open challenges and recommendations, and new research direction. J Circuits Syst Comput. 2018;27(05):1830003.

    Article  Google Scholar 

  22. Kalid N, Zaidan A, Zaidan B, Salman OH, Hashim M, Muzammil H. 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. 2018;42(2):30.

    Article  Google Scholar 

  23. Chang MY, Pang C, Michael Tarn J, Liu TS, Yen DC. Exploring user acceptance of an e-hospital service: An empirical study in Taiwan. Comput Stand Interfaces. 2015;38:35–43. Available from: http://www.sciencedirect.com/science/article/pii/S0920548914000828.

    Article  Google Scholar 

  24. Salman OH, Zaidan A, Zaidan B, Naserkalid, Hashim M. Novel methodology for triage and prioritizing using “big data” patients with chronic heart diseases through telemedicine environmental. International Journal of Information Technology & Decision Making. 2017;16(05):1211–45.

  25. Alsalem M, Zaidan A, Zaidan B, Hashim M, Madhloom H, Azeez N, et al. A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations. Comput Methods Programs Biomed. 2018;158:93–112.

    Article  Google Scholar 

  26. Abdulnabi M, Al-Haiqi A, Kiah MLM, Zaidan A, Zaidan B, Hussain M. A distributed framework for health information exchange using smartphone technologies. J Biomed Inform. 2017;69:230–50.

    Article  Google Scholar 

  27. Kalid N, Zaidan A, Zaidan B, Salman OH, Hashim M, Albahri OS, 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. 2018;42(4):1–37.

    Article  Google Scholar 

  28. Hussain M, Zaidan A, Zidan B, Iqbal S, Ahmed M, Albahri OS, et al. Conceptual framework for the security of mobile health applications on android platform. Telematics Inform. 2018;35(5):1335–54.

    Article  Google Scholar 

  29. Albahri OS, Albahri AS, Mohammed K, Zaidan A, Zaidan B, Hashim M, 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. 2018;42(5):1–27.

    Article  Google Scholar 

  30. Mohsin A, Zaidan A, Zaidan B, Albahri AS, Albahri OS, Alsalem M, 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. 2018;42(12):1–36.

    Article  Google Scholar 

  31. Shuwandy ML, Zaidan B, Zaidan A, Albahri AS. Sensor-based mHealth authentication for real-time remote healthcare monitoring system: A multilayer systematic review. J Med Syst. 2019;43(2):33.

    Article  Google Scholar 

  32. Alamoodi A, Zaidan B, Zaidan AA, Samuri SM, Ismail AR, Zughoul O, et al. A review of data analysis for early-childhood period: taxonomy, motivations, challenges, recommendation, and methodological aspects. IEEE Access. 2019;7:51069–103.

    Article  Google Scholar 

  33. Mohsin A, Zaidan A, Zaidan B, Mohammed K, Albahri OS, Albahri AS, et al. PSO–Blockchain-based image steganography: towards a new method to secure updating and sharing COVID-19 data in decentralised hospitals intelligence architecture. Multimedia tools and applications. 2021;80(9):14137–61.

    Article  Google Scholar 

  34. Kalid N, Zaidan AA, Zaidan BB, Salman OH, Hashim M, Albahri OS, 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. 2018;42(4):69. Available from: https://www.sciencedirect.com/science/article/pii/S0736585317308225.

    Article  Google Scholar 

  35. Mohsin A, Zaidan A, Zaidan B, Albahri OS, Albahri AS, Alsalem M, et al. Blockchain authentication of network applications: Taxonomy, classification, capabilities, open challenges, motivations, recommendations and future directions. Computer Standards & Interfaces. 2019;64:41–60.

    Article  Google Scholar 

  36. Alamoodi A, Garfan S, Zaidan B, Zaidan A, Shuwandy ML, Alaa M, et al. A systematic review into the assessment of medical apps: Motivations, challenges, recommendations and methodological aspect. Health and Technology. 2020:1–17.

  37. Albahri AS, Hamid RA, Alwan JK, Al-Qays Z, Zaidan A, Zaidan B, et al. Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review. J Med Syst. 2020;44:1–11.

    Article  Google Scholar 

  38. Mohsin A, Zaidan A, Zaidan B, Albahri O, Ariffin SAB, Alemran A, et al. Finger vein biometrics: taxonomy analysis, open challenges, future directions, and recommended solution for decentralised network architectures. IEEE Access. 2020;8:9821–45.

    Article  Google Scholar 

  39. Mohammed K, Zaidan A, Zaidan B, Albahri OS, Albahri AS, Alsalem M, et al. Novel technique for reorganisation of opinion order to interval levels for solving several instances representing prioritisation in patients with multiple chronic diseases. Computer methods and programs in biomedicine. 2020;185:105151.

  40. Mohsin A, Zaidan A, Zaidan B, bin Ariffin SA, Albahri OS, Albahri AS, et al. Real-time medical systems based on human biometric steganography: A systematic review. Journal of medical systems. 2018;42(12):1–20.

  41. Iqbal S, Kiah MLM, Zaidan A, Zaidan B, Albahri O, Albahri A, et al. Real-time-based E-health systems: Design and implementation of a lightweight key management protocol for securing sensitive information of patients. Heal Technol. 2019;9(2):93–111.

    Article  Google Scholar 

  42. Mohsin A, Zaidan A, Zaidan B, Albahri OS, Albahri AS, Alsalem M, et al. Based medical systems for patient’s authentication: Towards a new verification secure framework using CIA standard. J Med Syst. 2019;43(7):1–34.

    Article  Google Scholar 

  43. Albahri OS, Zaidan AA, Zaidan BB, Hashim M, Albahri AS, Alsalem MA, et al. Based Multiple Heterogeneous Wearable Sensors : A smart Real-Time Health-Monitoring Structured for Hospitals Distributor. IEEE Access. 2019;7:1–1.

    Article  Google Scholar 

  44. Shuwandy ML, Zaidan B, Zaidan A, Albahri AS, Alamoodi A, Albahri OS, et al. mHealth authentication approach based 3D touchscreen and microphone sensors for real-time remote healthcare monitoring system: comprehensive review, open issues and methodological aspects. Computer Science Review. 2020;38:100300.

  45. Mohammed K, Zaidan A, Zaidan B, Albahri OS, Alsalem M, Albahri AS, et al. Real-time remote-health monitoring systems: a review on patients prioritisation for multiple-chronic diseases, taxonomy analysis, concerns and solution procedure. J Med Syst. 2019;43(7):1–21.

    Article  Google Scholar 

  46. Mohsin A, Zaidan A, Zaidan B, Albahri OS, Albahri AS, Alsalem M, et al. Based blockchain-PSO-AES techniques in finger vein biometrics: A novel verification secure framework for patient authentication. Computer Standards & Interfaces. 2019;66:103343.

  47. Napi N, Zaidan A, Zaidan B, Albahri O, Alsalem M, Albahri A. Medical emergency triage and patient prioritisation in a telemedicine environment: a systematic review. Heal Technol. 2019;9(5):679–700.

    Article  Google Scholar 

  48. Albahri AS, Alwan JK, Taha ZK, Ismail SF, Hamid RA, Zaidan A, et al. IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art. Journal of Network and Computer Applications. 2021;173:102873.

  49. Zaidan AA, Zaidan BB, Qahtan M, Albahri OS, Albahri AS, Alaa M, et al. A survey on communication components for IoT-based technologies in smart homes. Telecommun Syst. 2018;69(1):1–25.

    Article  Google Scholar 

  50. Alaa M, Zaidan AA, Zaidan BB, Talal M, Kiah MLM. A review of smart home applications based on Internet of Things. J Netw Comput Appl. 2017;97:48–65.

    Article  Google Scholar 

  51. Talal M, Zaidan A, Zaidan B, Albahri AS, Alamoodi A, Albahri OS, 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. 2019;43(3):42.

    Article  Google Scholar 

  52. Zaidan A, Zaidan B. A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations. Artif Intell Rev. 2020;53(1):141–65.

    Article  Google Scholar 

  53. Farahani B, Firouzi F, Chang V, Badaroglu M, Constant N, Mankodiya K. Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Futur Gener Comput Syst. 2018;78(7–8):659–76.

    Article  Google Scholar 

  54. Rahmani AM, Gia TN, Negash B, Anzanpour A, Azimi I, Jiang M, et al. Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Futur Gener Comput Syst. 2018;78:641–58.

    Article  Google Scholar 

  55. Albahri AS, Alwan JK, Taha ZK, Ismail SF, Hamid RA, Zaidan AA, et al. IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art. J Netw Comput Appl. 2021;173:10287.

    Article  Google Scholar 

  56. Ozdemir AT, Tunc C, Hariri S. Autonomic fall detection system. In: Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017. p. 166–70.

  57. Acampora G, Cook DJ, Rashidi P, Vasilakos A V. A survey on ambient intelligence in healthcare. Proc IEEE . 2013;101(12):2470–94. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3890262&tool=pmcentrez&rendertype=abstract.

  58. McAllister TD, El-Tawab S, Heydari MH. Localization of Health Center Assets Through an IoT Environment (LoCATE). In: 2017 Systems and Information Engineering Design Symposium, SIEDS 2017. 2017. p. 132–7.

  59. Gia TN, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H. Fault tolerant and scalable IoT-based architecture for health monitoring. In: SAS 2015 - 2015 IEEE Sensors Applications Symposium, Proceedings. 2015. p. 334–9.

  60. Jin Z, Chen Y. Telemedicine in the cloud era: Prospects and challenges. IEEE Pervasive Comput [Internet]. 2015 Jan;14(1):54–61. Available from: http://ieeexplore.ieee.org/document/7030248/.

  61. Sene A, Kamsu-Foguem B, Rumeau P. Telemedicine framework using case-based reasoning with evidences. Comput Methods Programs Biomed [Internet]. 2015 Aug;121(1):21–35. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0169260715001108.

  62. Dong J, Yang G-H. Reliable State Feedback Control of T-S Fuzzy Systems With Sensor Faults. IEEE Trans Fuzzy Syst. 2015;23(2):421–33.

    Article  MathSciNet  Google Scholar 

  63. Salman OH, Rasid MFA, Saripan MI, Subramaniam SK. Multi-sources data fusion framework for remote triage prioritization in telehealth. J Med Syst [Internet]. 2014 Sep 22;38(9):103. Available from: http://link.springer.com/10.1007/s10916-014-0103-4.

  64. Albahri AS, Zaidan AA, Albahri OS, Zaidan BB, Alsalem MA. Real-Time Fault-Tolerant mHealth System: Comprehensive Review of Healthcare Services, Opens Issues, Challenges and Methodological Aspects. J Med Syst [Internet]. 2018 Jun 23 [cited 2018 Oct 20];42(8):137. Available from: http://link.springer.com/10.1007/s10916-018-0983-9.

  65. Escobar-Curbelo L, Franco-Moreno AI. Application of Telemedicine for the Control of Patients with Acute and Chronic Heart Diseases. Telemed e-Health. 2019;25(11):1033–9.

    Article  Google Scholar 

  66. Moser DK, Kimble LP, Alberts MJ, Alonzo A, Croft JB, Dracup K, et al. Reducing delay in seeking treatment by patients with acute coronary syndrome and stroke: A scientific statement from the American Heart Association Council on Cardiovascular Nursing and Stroke Council. Circulation. 2006;114(2):168–82.

    Article  Google Scholar 

  67. Anonymous. Report of the Ad Hoc Committee on Health Research Relating to Future Intervention Options. Investing in Health Research and Development. Vol. TDR/Gen 96. Geneva: World Health Organization; 1996.

  68. Derlet RW, Kinser D, Ray L, Hamilton B, McKenzie J. Prospective Identification and Triage of Nonemergency Patients Out of an Emergency Department: A 5-Year Study. Ann Emerg Med. 1995;25(2):215–23.

    Article  Google Scholar 

  69. Tzeng GH, Huang JJ. Multiple attribute decision making: Methods and applications. Mult Attrib Decis Mak Methods Appl. 2011;1–333.

  70. Gogan JL, Davidson EJ, Proudfoot J. The HealthCare.gov project. J Inf Technol Teach Cases. 2016 Nov;6(2):99–110.

  71. Wood A. Predicting client/server availability. Computer (Long Beach Calif). 1995;28(4):41–8.

    Google Scholar 

  72. Sobhani F, Xu C, Murano E, Pan L, Rastegar N, Kamel IR. Hypo-vascular liver metastases treated with transarterial chemoembolization: Assessment of early response by volumetric contrast-enhanced and diffusion-weighted magnetic resonance imaging. Transl Oncol. 2016;9(4):287–94. Available from: http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L611845410%0Ahttp://dx.doi.org/10.1016/j.tranon.2016.03.005%0Ahttp://rug.on.worldcat.org/atoztitles/link/?sid=EMBASE&issn=19365233&id=doi:10.1016%2Fj.tranon.2016.03.005&atitle=Hypo-v.

    Article  Google Scholar 

  73. Hossain MS, Muhammad G. Cloud-assisted Industrial Internet of Things (IIoT) - Enabled framework for health monitoring. Comput Networks [Internet]. 2016;101(0):192–202. Available from: https://pdfs.semanticscholar.org/a4f3/8719e4e20c25ac1f6cd51f31b38d50264590.pdf.

  74. Broach J, Hart A, Griswold M, Lai J, Boyer EW, Skolnik AB, et al. Usability and Reliability of Smart Glasses for Secondary Triage During Mass Casualty Incidents. Proc Annu Hawaii Int Conf Syst Sci [Internet]. 2018 [cited 2018 Nov 26];1416–22. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5794491/.

  75. Paulus A, Meisen P, Meisen T, Jeschke S, Czaplik M, Hirsch F. AUDIME: Augmented disaster medicine. In: 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015. 2015. p. 342–5.

  76. Paulus A, Meisen P, Meisen T, Jeschke S, Czaplik M, Hirsch F. AUDIME: Augmented disaster medicine. In: 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015 [Internet]. Cham: IEEE; 2016. p. 342–5. Available from: http://link.springer.com/10.1007/978-3-319-42620-4_47.

  77. Beck C, Georgiou J. A wearable, multimodal, vitals acquisition unit for intelligent field triage. Proc - IEEE Int Symp Circuits Syst [Internet]. 2016 Sep 1;2016-July(3):1530–3. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27733926.

  78. Besaleva LI, Weaver AC, CrowdHelp: M-Health application for emergency response improvement through crowdsourced and sensor-detected information. In, Wireless Telecommunications Symposium. New York, New York, USA: IEEE. 2014;2014:1–5.

    Google Scholar 

  79. Ganz A, Schafer JM, Tang J, Yang Z, Yi J, Ciottone G. Urban Search and Rescue Situational Awareness using DIORAMA Disaster Management System. Procedia Eng [Internet]. 2015;107:349–56. Available from: http://linkinghub.elsevier.com/retrieve/pii/S1877705815010449.

  80. Renner A, Williams R, McCartney M, Harmon B, Boswell L, Ganapathy S, et al. RIPPLE: Scalable medical telemetry system for supporting combat rescue. Natl Aerosp Electron Conf Proc IEEE. 2015;2015-Febru:228–32.

  81. Gunasekaran S, Suresh M. A novel control of disaster protection (NCDP) for pilgrims by pan technology. In: 2014 IEEE 8th International Conference on Intelligent Systems and Control: Green Challenges and Smart Solutions, ISCO 2014 - Proceedings [Internet]. IEEE; 2014. p. 103–7. Available from: http://ieeexplore.ieee.org/document/7103927/.

  82. Adibi S. A mobile health network disaster management system. In: International Conference on Ubiquitous and Future Networks, ICUFN [Internet]. IEEE; 2015. p. 424–8. Available from: http://ieeexplore.ieee.org/document/7182579/.

  83. Sneha S, Varshney U. A framework for enabling patient monitoring via mobile ad hoc network. Decis Support Syst [Internet]. 2013 Apr;55(1):218–34. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0167923613000523.

  84. Fratini A, Caleffi M. Medical emergency alarm dissemination in urban environments. Telemat Informatics. 2014;31(3):511–7.

    Article  Google Scholar 

  85. Albahri OS, Albahri AS, Zaidan AA, Zaidan BB, Alsalem MA, Mohsin AH, et al. Fault-Tolerant mHealth Framework in the Context of IoT-Based Real-Time Wearable Health Data Sensors. IEEE Access [Internet]. 2019;7:50052–80. Available from: https://ieeexplore.ieee.org/document/8688649/.

  86. Kovalchuk SV, Krotov E, Smirnov PA, Nasonov DA, Yakovlev AN. Distributed data-driven platform for urgent decision making in cardiological ambulance control. Futur Gener Comput Syst. 2018;79:144–54.

    Article  Google Scholar 

  87. Mohammed KI, Zaidan AA, Zaidan BB, Albahri OS, Albahri AS, Alsalem MA, et al. Novel technique for reorganisation of opinion order to interval levels for solving several instances representing prioritisation in patients with multiple chronic diseases. Comput Methods Programs Biomed. 2020;185:105151.

  88. Chen H, Qin R. Revenue management of transportation infrastructure during the service life using real options. In: Decision Making in Service Industries: A Practical Approach. CRC Press; 2012. p. 257–78.

  89. Alsalem MA, Zaidan AA, Zaidan BB, Albahri OS, Alamoodi AH, Albahri AS, et al. Multiclass Benchmarking Framework for Automated Acute Leukaemia Detection and Classification Based on BWM and Group-VIKOR. J Med Syst. 2019;43(7):212.

    Article  Google Scholar 

  90. Berglas NF, Battistelli MF, Nicholson WK, Sobota M, Urman RD, Roberts SCM. The effect of facility characteristics on patient safety, patient experience, and service availability for procedures in non-hospital-affiliated outpatient settings: A systematic review. PLoS One. 2018;13(1):e0190975.

  91. Busse R, Schreyögg J, Smith PC. Variability in healthcare treatment costs amongst nine eu countries - Results from the healthbasket project. Health Econ. 2008;17(SUPPL. 1):S1-8.

    Article  Google Scholar 

  92. Sentz K, Ferson S. Combination of Evidence in Dempster- Shafer Theory [Internet]. Vol. 4015, Contract. Sandia National Laboratories Albuquerque; 2002. 96 p. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.122.7929&rep=rep1&type=pdf.

  93. Shafer G. Dempster-shafer theory. Encycl. Artif Intell. 1992;1:330–1.

    Google Scholar 

  94. Mohammed R, Yaakob R, Zaidan A, Sharef N, Abdullah R, Zaidan B, et al. Review of the research landscape of multi-criteria evaluation and benchmarking processes for many-objective optimization methods: coherent taxonomy, challenges and recommended solution. International Journal of Information Technology & Decision Making (IJITDM). 2020;19(06):1619–93.

    Article  Google Scholar 

  95. Salih MM, Zaidan B, Zaidan A. Fuzzy decision by opinion score method. Applied Soft Computing. 2020;96:106595.

  96. Mohammed TJ, Albahri AS, Zaidan A, Albahri OS, Al-Obaidi JR, Zaidan B, et al. Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component. Appl Intell. 2021;51(5):2956–87.

    Article  Google Scholar 

  97. Krishnan E, Mohammed R, Alnoor A, Albahri OS, Zaidan AA, Alsattar H, et al. Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications. International Journal of Intelligent Systems. 2021.

  98. Salih MM, Albahri O, Zaidan A, Zaidan B, Jumaah F, Albahri A. Benchmarking of AQM methods of network congestion control based on extension of interval type-2 trapezoidal fuzzy decision by opinion score method. Telecommun Syst. 2021;77(3):493–522.

    Article  Google Scholar 

  99. Albahri AS, Al-Obaidi JR, Zaidan A, Albahri OS, Hamid RA, Zaidan B, et al. Multi-biological laboratory examination framework for the prioritization of patients with COVID-19 based on integrated AHP and group VIKOR methods. Int J Inf Technol Decis Mak. 2020;19(05):1247–69.

    Article  Google Scholar 

  100. Abdulkareem KH, Arbaiy N, Zaidan A, Zaidan B, Albahri OS, Alsalem M, et al. A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods. Neural Comput Appl. 2021;33:1029–54.

    Article  Google Scholar 

  101. Albahri O, Zaidan A, Albahri A, Zaidan B, Abdulkareem KH, Al-Qaysi Z, et al. Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of infection and public health. 2020.

  102. Albahri OS, Al-Obaidi JR, Zaidan A, Albahri AS, Zaidan B, Salih MM, et al. Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods. Computer methods and programs in biomedicine. 2020;196:105617.

  103. Zughoul O, Momani F, Almasri O, Zaidan A, Zaidan B, Alsalem M, et al. Comprehensive insights into the criteria of student performance in various educational domains. IEEE access. 2018;6:73245–64.

    Article  Google Scholar 

  104. Zaidan A, Zaidan B, Alsalem M, Momani F, Zughoul O. Novel Multiperspective Hiring Framework for the Selection of Software Programmer Applicants Based on AHP and Group TOPSIS Techniques. Int J Inf Technol Decis Mak. 2020;19(03):775–847.

    Article  Google Scholar 

  105. Abdulkareem KH, Arbaiy N, Zaidan A, Zaidan B, Albahri OS, Alsalem M, et al. A novel multi-perspective benchmarking framework for selecting image dehazing intelligent algorithms based on BWM and group VIKOR techniques. Int J Inf Technol Decis Mak. 2020;19(03):909–57.

    Article  Google Scholar 

  106. Hamid RA, Albahri AS, Alwan JK, Al-qaysi Z, Albahri OS, Zaidan A, et al. How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management. Computer Science Review. 2021;39:100337.

  107. Albahri O, Zaidan A, Zaidan B, Albahri A, Mohsin A, Mohammed K, et al. New mHealth hospital selection framework supporting decentralised telemedicine architecture for outpatient cardiovascular disease-based integrated techniques: Haversine-GPS and AHP-VIKOR. Journal of Ambient Intelligence and Humanized Computing. 2021:1–21.

  108. Malik R, Zaidan A, Zaidan B, Ramli K, Albahri O, Kareem Z, et al. Novel roadside unit positioning framework in the context of the vehicle-to-infrastructure communication system based on AHP—Entropy for weighting and borda—VIKOR for uniform ranking. International Journal of Information Technology & Decision Making. 2021:1–34.

  109. Khatari M, Zaidan A, Zaidan B, Albahri O, Alsalem M, Albahri A. Multidimensional benchmarking framework for AQMs of network congestion control based on AHP and Group-TOPSIS. International Journal of Information Technology & Decision Making. 2021:1–38.

  110. Mohammed R, Zaidan A, Yaakob R, Sharef N, Abdullah R, Zaidan B, et al. Determining importance of many-objective optimisation competitive algorithms evaluation criteria based on a novel fuzzy-weighted zero-inconsistency method. International Journal of Information Technology & Decision Making. 2021:1–47.

  111. Dawood KA, Zaidan A, Sharif KY, Ghani AA, Zulzalil H, Zaidan B. Novel multi-perspective usability evaluation framework for selection of open source software based on BWM and group VIKOR techniques. International Journal of Information Technology & Decision Making. 2021:1–91.

  112. Albahri AS, Albahri OS, Zaidan A, Zaidan B, Hashim M, Alsalem M, et al. Based multiple heterogeneous wearable sensors: A smart real-time health monitoring structured for hospitals distributor. IEEE access. 2019;7:37269–323.

    Article  Google Scholar 

  113. Albahri OS, Zaidan AA, Salih MM, Zaidan BB, Khatari MA, Ahmed MA, et al. Multidimensional benchmarking of the active queue management methods of network congestion control based on extension of fuzzy decision by opinion score method. Int J Intell Syst. 2021;36(2):796–831.

    Article  Google Scholar 

  114. Albahri AS, Hamid RA, Albahri OS, Zaidan A. Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods. Artificial intelligence in medicine. 2021;111:101983.

  115. Zughoul O, Zaidan A, Zaidan B, Albahri OS, Alazab M, Amomeni U, et al. Novel triplex procedure for ranking the ability of software engineering students based on two levels of AHP and group TOPSIS techniques. International Journal of Information Technology & Decision Making (IJITDM). 2021;20(01):67–135.

    Article  Google Scholar 

  116. Dawood KA, Sharif KY, Ghani AA, Zulzalil H, Zaidan A, Zaidan B. Towards a unified criteria model for usability evaluation in the context of open source software based on a fuzzy Delphi method. Information and Software Technology. 2021;130:106453.

  117. Albahri OS, Zaidan A, Zaidan B, Hashim M, Albahri AS, Alsalem M. 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. 2018;42(9):1–47.

    Article  Google Scholar 

  118. Albahri AS, Zaidan A, Albahri OS, Zaidan B, Alsalem M. Real-time fault-tolerant mHealth system: Comprehensive review of healthcare services, opens issues, challenges and methodological aspects. J Med Syst. 2018;42(8):1–56.

    Article  Google Scholar 

  119. Enaizan O, Zaidan AA, Alwi NM, Zaidan BB, Alsalem MA, Albahri O, et al. Electronic medical record systems: Decision support examination framework for individual, security and privacy concerns using multi-perspective analysis. Heal Technol. 2020;10(3):795–822.

    Article  Google Scholar 

  120. Salih MM, Zaidan B, Zaidan A, Ahmed MA. Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017. Comput Oper Res. 2019;104:207–27.

    Article  MathSciNet  MATH  Google Scholar 

  121. Zaidan A, Zaidan B, Alsalem M, Albahri OS, Albahri AS, Qahtan M. Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology. Neural Comput Appl. 2020;32(12):8315–66.

    Article  Google Scholar 

  122. AlSattar H, Zaidan A, Zaidan B, Bakar MA, Mohammed R, Albahri O, et al. MOGSABAT: A metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Comput Appl. 2020;32(8):3101–15.

    Article  Google Scholar 

  123. Albahri OS, Albahri AS, Zaidan A, Zaidan B, Alsalem M, Mohsin A, et al. Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors. IEEE Access. 2019;7:50052–80.

    Article  Google Scholar 

  124. Rahmatullah B, Zaidan A, Mohamed F, Sali A, editors. Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection. 2017 4th international conference on control, decision and information technologies (CoDIT); 2017: IEEE.

  125. Zaidan B, Zaidan A, Abdul Karim H, Ahmad N. A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques. International Journal of Information Technology & Decision Making. 2017:1–42.

  126. Zaidan A, Zaidan B, Hussain M, Haiqi A, Kiah MM, Abdulnabi M. Multi-criteria analysis for OS-EMR software selection problem: A comparative study. Decis Support Syst. 2015;78:15–27.

    Article  Google Scholar 

  127. Chen S-J, Hwang C-L. Fuzzy Multiple Attribute Decision Making Methods. In: Fuzzy multiple attribute decision making [Internet]. Springer; 1992. p. 289–486. Available from: http://link.springer.com/10.1007/978-3-642-46768-4_5.

  128. Saaty TL, Ozdemir MS. Why the magic number seven plus or minus two. Math Comput Model [Internet]. 2003;38(3–4):233–44. Available from: www.elsevier.com/locatp!mcm.

  129. Sherekar V, Tatikonda M. Impact of Factor Affecting on Labour Productivity in Construction Projects by AHP Method. Int J Eng Sci Comput. 2016;6(6):6771–5.

    Google Scholar 

  130. Alaa M, Albakri ISMA, Singh CKS, Hammed H, Zaidan A, Zaidan B, et al. Assessment and ranking framework for the English skills of pre-service teachers based on fuzzy Delphi and TOPSIS methods. IEEE Access. 2019;7:126201–23.

    Article  Google Scholar 

  131. Talal M, Zaidan A, Zaidan B, Albahri OS, Alsalem M, Albahri AS, et al. Comprehensive review and analysis of anti-malware apps for smartphones. Telecommun Syst. 2019;72(2):285–337.

    Article  Google Scholar 

  132. Ibrahim N, Hammed H, Zaidan A, Zaidan B, Albahri OS, Alsalem M, et al. Multi-criteria evaluation and benchmarking for young learners’ English language mobile applications in terms of LSRW skills. IEEE Access. 2019;7:146620–51.

    Article  Google Scholar 

  133. Khatari M, Zaidan A, Zaidan B, Albahri OS, Alsalem M. Multi-criteria evaluation and benchmarking for active queue management methods: Open issues, challenges and recommended pathway solutions. Int J Inf Technol Decis Mak. 2019;18(04):1187–242.

    Article  Google Scholar 

  134. Almahdi E, Zaidan A, Zaidan B, Alsalem M, Albahri OS, Albahri AS. Mobile patient monitoring systems from a benchmarking aspect: Challenges, open issues and recommended solutions. J Med Syst. 2019;43(7):1–23.

    Article  Google Scholar 

  135. Alsalem M, Zaidan A, Zaidan B, Albahri OS, Alamoodi A, Albahri AS, et al. Multiclass benchmarking framework for automated acute Leukaemia detection and classification based on BWM and group-VIKOR. J Med Syst. 2019;43(7):1–32.

    Article  Google Scholar 

  136. Almahdi E, Zaidan A, Zaidan B, Alsalem M, Albahri OS, Albahri AS. Mobile-based patient monitoring systems: A prioritisation framework using multi-criteria decision-making techniques. J Med Syst. 2019;43(7):1–19.

    Article  Google Scholar 

  137. Jumaah F, Zadain A, Zaidan B, Hamzah A, Bahbibi R. 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. 2018;118:83–95.

    Article  Google Scholar 

  138. Alsalem M, Zaidan A, Zaidan B, Hashim M, Albahri OS, Albahri AS, 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. 2018;42(11):1–36.

    Article  Google Scholar 

  139. Zaidan A, Zaidan B, Albahri O, Alsalem M, Albahri A, Yas QM, et al. A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution. Heal Technol. 2018;8(4):223–38.

    Article  Google Scholar 

  140. Zaidan AA, Zaidan BB, Al-Haiqi A, Kiah MLM, Hussain M, Abdulnabi M. Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. J Biomed Inform. 2015;53:390–404.

    Article  Google Scholar 

  141. Abdullateef BN, Elias NF, Mohamed H, Zaidan A, Zaidan B. An evaluation and selection problems of OSS-LMS packages. Springerplus. 2016;5(1):1–35.

    Article  Google Scholar 

  142. Zaidan B, Zaidan A. Software and hardware FPGA-based digital watermarking and steganography approaches: Toward new methodology for evaluation and benchmarking using multi-criteria decision-making techniques. Journal of Circuits, Systems and Computers. 2017;26(07):1750116.

    Article  Google Scholar 

  143. Yas QM, Zadain A, Zaidan B, Lakulu M, Rahmatullah B. 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. 2017;31(03):1759002.

    Article  Google Scholar 

  144. Jumaah F, Zaidan A, Zaidan B, Bahbibi R, Qahtan M, Sali A. 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. 2018;68(3):425–43.

    Article  Google Scholar 

  145. Zaidan B, Zaidan A, Karim HA, Ahmad NN. A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi‐criteria analysis based on ‘large‐scale data’. Software: Practice and Experience. 2017;47(10):1365–92.

  146. Yas QM, Zaidan A, Zaidan B, Rahmatullah B, Karim HA. Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions. Measurement. 2018;114:243–60.

    Article  Google Scholar 

  147. Zaidan B, Zaidan 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. 2018;117:277–94.

    Article  Google Scholar 

  148. Saksrisathaporn K, Bouras A, Reeveerakul N, Charles A. Application of a Decision Model by Using an Integration of AHP and TOPSIS Approaches within Humanitarian Operation Life Cycle. Int J Inf Technol Decis Mak. 2016;15(04):887–918.

    Article  Google Scholar 

  149. Almahdi EM, Zaidan AA, Zaidan BB, Alsalem MA, Albahri OS, Albahri AS. Mobile-Based Patient Monitoring Systems: A Prioritisation Framework Using Multi-Criteria Decision-Making Techniques. J Med Syst. 2019;43(7):219.

    Article  Google Scholar 

  150. Albahri AS, Hamid RA, Albahri OS, Zaidan AA. Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods. Artif Intell Med. 2021;111:101983.

  151. Albahri OS, Zaidan AA, Zaidan BB, Hashim M, Albahri AS, Alsalem MA. 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 [Internet]. 2018 Sep 25 [cited 2018 Oct 20];42(9):164. Available from: http://link.springer.com/10.1007/s10916-018-1006-6.

  152. Albahri OS, Zaidan AA, Zaidan BB, Hashim M, Albahri AS, Alsalem MA, et al. Based Multiple Heterogeneous Wearable Sensors : A smart Real-Time Health-Monitoring Structured for Hospitals Distributor. IEEE Access [Internet]. 2019 [cited 2019 Apr 13];1–1. Available from: https://ieeexplore.ieee.org/abstract/document/8638775/.

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Acknowledgements

The authors are grateful to the Universiti Pendidikan Sultan Idris, Malaysia for funding this study under UPSI Rising Star Grant No. 2019-0125-109-01.

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Albahri, A.S., Zaidan, A.A., Albahri, O.S. et al. Development of IoT-based mhealth framework for various cases of heart disease patients. Health Technol. 11, 1013–1033 (2021). https://doi.org/10.1007/s12553-021-00579-x

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