UPSI Digital Repository (UDRep)
|
|
|
Abstract : Universiti Pendidikan Sultan Idris |
An intelligent remote prioritization for patients with high-risk multiple chronic diseases is proposed in this research, based on emotion and sensory measurements and multi-criteria decision making. The methodology comprises two phases: (1) a case study is discussed through the adoption of a multi-criteria decision matrix for high-risk level patients; (2) the technique for reorganizing opinion order to interval levels (TROOIL) is modified by combining it with an extended fuzzy-weighted zero-inconsistency (FWZIC) method over fractional orthotriple fuzzy sets to address objective weighting issues associated with the original TROOIL. In the first hierarchy level, chronic heart disease is identified as the most important criterion, followed by emotion-based criteria in the second. The third hierarchy level shows that Peaks is identified as the most important sensor-based criterion and chest pain as the most important emotion criterion. Low blood pressure disease is identified as the most important criterion for patient prioritization, with the most severe cases being prioritized. The results are evaluated using systematic ranking and sensitivity analysis. 2023 by the authors. |
References |
Samal, L.; Fu, H.N.; Camara, D.S.; Wang, J.; Bierman, A.S.; Dorr, D.A. Health information technology to improve care for people with multiple chronic conditions. Health Serv. Res. 2021, 56, 1006–1036. [CrossRef] [PubMed] Kim, B.Y.; Lee, J. Smart devices for older adults managing chronic disease: A scoping review. JMIR Mhealth Uhealth 2017, 5, e7141.[CrossRef] Fletcher, P.C.; Guthrie, D.M.; Berg, K.; Hirdes, J.P. Risk factors for restriction in activity associated with fear of falling among seniors within the community. J. Patient Saf. 2010, 6, 187–191. [CrossRef] [PubMed] Hung, W.W.; Ross, J.S.; Boockvar, K.S.; Siu, A.L. Recent trends in chronic disease, impairment and disability among older adults in the United States. BMC Geriatr. 2011, 11, 47. [CrossRef] [PubMed] van den Akker, M.; Buntinx, F.; Knottnerus, J.A. Comorbidity or multimorbidity: What’s in a name? A review of literature. Eur. J. Gen. Pract. 1996, 2, 65–70. [CrossRef] Nave, O.; Sigron, M. A mathematical model for cancer treatment based on combination of anti-angiogenic and immune cell therapies. Results Appl. Math. 2022, 16, 100330. [CrossRef] 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, 7. [CrossRef] Garfan, S.; Alamoodi, A.; Zaidan, B.; Al-Zobbi, M.; Hamid, R.A.; Alwan, J.K.; Ahmaro, I.Y.; Khalid, E.T.; Jumaah, F.; Albahri, O. Telehealth utilization during the Covid-19 pandemic: A systematic review. Comput. Biol. Med. 2021, 138, 104878. [CrossRef] Ray, P.P. Understanding the role of internet of things towards smart e-healthcare services. Biomed. Res. 2017, 28, 1604–1609. Albahri, O.S.; Albahri, A.S.; Zaidan, A.; Zaidan, B.; Alsalem, M.; Mohsin, A.; Mohammed, K.; Alamoodi, A.; Nidhal, S.; Enaizan, O. Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors. IEEE Access 2019, 7, 50052–50080. [CrossRef] Salman, O.; Rasid, M.F.A.; Saripan, M.I.; Subramaniam, S.K. Multi-sources data fusion framework for remote triage prioritization in telehealth. J. Med. Syst. 2014, 38, 103. [CrossRef] Rocha, A.; Martins, A.; Junior, J.C.F.; Boulos, M.N.K.; Vicente, M.E.; Feld, R.; van de Ven, P.; Nelson, J.; Bourke, A.; ÓLaighin, G. Innovations in health care services: The CAALYX system. Int. J. Med. Inform. 2013, 82, e307–e320. [CrossRef] Mohammed, K.; Zaidan, A.; Zaidan, B.; Albahri, O.S.; Albahri, A.S.; Alsalem, M.; Mohsin, A. 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. [CrossRef] Mohammed, K.; Jaafar, J.; Zaidan, A.; Albahri, O.S.; Zaidan, B.; Abdulkareem, K.H.; Jasim, A.N.; Shareef, A.H.; Baqer, M.; Albahri, A.S. 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, 91521–91530. [CrossRef] Wang, T.-C.; Lee, H.-D. Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Syst. Appl. 2009, 36, 8980–8985. [CrossRef] Nigim, K.; Munier, N.; Green, J. Pre-feasibility MCDM tools to aid communities in prioritizing local viable renewable energy sources. Renew. Energy 2004, 29, 1775–1791. [CrossRef] Mohammed, T.J.; Albahri, A.S.; Zaidan, A.; Albahri, O.S.; Al-Obaidi, J.R.; Zaidan, B.; Larbani, M.; Mohammed, R.; Hadi, S.M. 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, 2956–2987. [CrossRef] Rezaei, J. Best-worst multi-criteria decision-making method. Omega 2015, 53, 49–57. [CrossRef] Alsalem, M.; Alsattar, H.; Albahri, A.; Mohammed, R.; Albahri, O.; Zaidan, A.; Alnoor, A.; Alamoodi, A.; Qahtan, S.; Zaidan, B. Based on T-spherical Fuzzy Environment: A Combination of FWZIC and FDOSM for Prioritising COVID-19 Vaccine Dose Recipients. J. Infect. Public Health 2021, 14, 1513–1559. [CrossRef] Krishnan, E.; Mohammed, R.; Alnoor, A.; Albahri, O.S.; Zaidan, A.A.; Alsattar, H.; Albahri, A.S.; Zaidan, B.B.; Kou, G.; Hamid, R.A. Interval type 2 trapezoidal-fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e-tourism applications. Int. J. Intell. Syst. 2021, 36, 4723–4774. [CrossRef] Albahri, A.; Albahri, O.; Zaidan, A.; Alnoor, A.; Alsatta, H.; Mohammed, R.; Alamoodi, A.; Zaidan, B.; Aickelin, U.; Alazab, M. Integration of Fuzzy-Weighted Zero-Inconsistency and Fuzzy Decision by Opinion Score Methods under a q-Rung Orthopair Environment: A Distribution Case Study of COVID-19 Vaccine Doses. Comput. Stand. Interfaces 2021, 80, 103572. [CrossRef] Naeem, M.; Qiyas, M.; Al-Shomrani, M.M.; Abdullah, S. Similarity measures for fractional orthotriple fuzzy sets using cosine and cotangent functions and their application in accident emergency response. Mathematics 2020, 8, 1653. [CrossRef] Abosuliman, S.S.; Abdullah, S.; Qiyas, M. Three-way decisions making using covering based fractional Orthotriple fuzzy rough set model. Mathematics 2020, 8, 1121. [CrossRef] Qiyas, M.; Abdullah, S.; Khan, F.; Naeem, M. Banzhaf-Choquet-Copula-based aggregation operators for managing fractional orthotriple fuzzy information. Alex. Eng. J. 2021, 61, 4659–4677. [CrossRef] 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. Int. J. Inf. Technol. Decis. Mak. 2021, 20, 1409–1446. [CrossRef] Pamucar, D.; Yazdani, M.; Obradovic, R.; Kumar, A.; Torres-Jiménez, M. A novel fuzzy hybrid neutrosophic decision-making approach for the resilient supplier selection problem. Int. J. Intell. Syst. 2020, 35, 1934–1986. [CrossRef] |
This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |