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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (0.1837, 0.183, 0.230, 0.276, 0.335) for (q = 1, 3, 5, 7, 10), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework. 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. |
| References |
Raad MW, Sheltami T, Shakshuki E (2015) Ubiquitous telehealth system for elderly patients with alzheimer’s. Procedia Comput Sci 52:685–689 Albahri O et al (2018) 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):80 Kalid N et al (2018) 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):69 Albahri A et al (2018) Real-time fault-tolerant mHealth system: comprehensive review of healthcare services, opens issues, challenges and methodological aspects. J Med Syst 42(8):137 Hamid RA et al (2021) Dempster–Shafer theory for classification and hybridised models of multi-criteria decision analysis for prioritisation: a telemedicine framework for patients with heart diseases. J Ambient Intell Humanized Comput 13(9):1–35 Jabeen F et al (2019) An IoT based efficient hybrid recommender system for cardiovascular disease. Peer-to-Peer Network Appl 12(5):1263–1276 Bhatt A, Dubey S, Bhatt A (2018) Analytical study on cardiovascular health issues prediction using decision model-based predictive analytic techniques. In: Pant M, Ray K, Sharma T, Rawat S, Bandyopadhyay A (eds) Soft Computing: Theories and Applications. Springer, Singapore, pp 289–299 Albahri O et al (2018) 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 Zheng T et al (2017) A machine learning-based framework to identify type 2 diabetes through electronic health records. Int J Med Informatics 97:120–127 Mohammed K et al (2019) Real-time remote-health monitoring systems: a review on patients prioritisation for multiple-chronic diseases, taxonomy analysis, concerns and solution procedure. J Med Syst 43(7):223 Sudhakar K, Manimekalai DM (2014) Study of heart disease prediction using data mining. Int J Adv Res Comput Sci Softw Eng 4(1):1157–1160 Napi NM et al (2019) Medical emergency triage and patient prioritisation in a telemedicine environment: a systematic review. Heal Technol 9(5):679–700. https://doi.org/10.1007/s12553-019-00357-w Albahri AS et al (2021) Development of IoT-based mhealth framework for various cases of heart disease patients. Health and Technol 11(5):1013–1033. https://doi.org/10.1007/s12553-021-00579-x Ray PP (2017) Understanding the role of internet of things towards smart e-healthcare services. Biomed Res 28(4):1604–1609 Albahri OS et al (2019) Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors. IEEE Access 7:50052–50080 Salman O, Rasid MFA, Saripan MI, Subramaniam SK (2014) Multi-sources data fusion framework for remote triage prioritization in telehealth. J Med Syst 38(9):1–23 Rocha A et al (2013) Innovations in health care services: The CAALYX system. Int J Med Informatics 82(11):e307–e320 Albahri AS et al (2019) Based multiple heterogeneous wearable sensors: A smart real-time health monitoring structured for hospitals distributor. IEEE access 7:37269–37323 Van Dyk L (2014) A review of telehealth service implementation frameworks. Int J Environ Res Public Health 11(2):1279–1298 Dong J, Yang G-H (2014) Reliable state feedback control of T-S fuzzy systems with sensor faults. IEEE Trans Fuzzy Syst 23(2):421–433 Almahdi E et al (2019) Mobile patient monitoring systems from a benchmarking aspect: challenges, open issues and recommended solutions. J Med Syst 43(7):207 Albahri O et al (2021) New mHealth hospital selection framework supporting decentralised telemedicine architecture for outpatient cardiovascular disease-based integrated techniques: Haversine-GPS and AHP-VIKOR. J Ambient Intell Humanized Comput 13(1):1–21 Mohammed K et al (2020) 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 185:105151 Mohammed K et al (2020) 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 8:91521–91530 Zaidan A et al (2018) A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution. Heal Technol 8(4):223–238 Alsalem M et al (2018) 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 Alsalem M et al (2019) Multiclass benchmarking framework for automated acute Leukaemia detection and classification based on BWM and group-VIKOR. J Med Syst 43(7):212 Alaa M et al (2019) Assessment and ranking framework for the English skills of pre-service teachers based on fuzzy Delphi and TOPSIS methods. IEEE Access 7:126201–126223 Ibrahim N et al (2019) Multi-criteria evaluation and benchmarking for young learners’ english language mobile applications in terms of LSRW skills. IEEE Access 7(7):146620–146651 Mohammed TJ et al (2021) Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHPgroup TOPSIS and matching component. Appl Intell 51(5):1–32 |
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