UPSI Digital Repository (UDRep)
Start | FAQ | About

QR Code Link :

Type :article
Subject :RA0421 Public health. Hygiene. Preventive Medicine
Main Author :Albahrey, Shihab Ahmed
Additional Authors :Al-Juboori, A. Z. Ansaef
Zaidan, Bilal Bahaa
Bilal Bahaa
K. I. Mohammed
Mohammed Ahmed Chyad
Odai Enaizan
Ali Hadi Mohsin
Alamoodi, A. H.
Title :Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2019
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Full Text :Login required to access this item.

Abstract : Universiti Pendidikan Sultan Idris
The emerging technology breakthrough of the Internet of Things (IoT) is expected to offer promising solutions for indoor/outdoor healthcare, which may contribute significantly to human health and well-being. In this paper, we investigated the technologies of healthcare service applications in telemedicine architecture. We aimed to resolve a series of healthcare problems on the frequent failures in telemedicine architecture through IoT solutions, particularly the failures of wearable body sensors (Tier 1) and a medical center server (Tier 3). For improved generalisability, we demonstrated an effective research approach, the fault-tolerant framework on mHealth or the so-called FTF-mHealth-IoT in the context of IoT, to resolve essential problems in current investigations on healthcare services. First, we propose a risk local triage algorithm known as the risk-level localization triage (RLLT), which can exclude the control process of patient triage from the medical center by using mHealth and can warn about failures related to wearable sensors. RLLT performs this initial step towards detecting a patient’s emergency case and then identifying the healthcare service package of the risk-level. Second, according to the risk-level package, our framework can aid decision makers in hospital selection through multi-criteria decision making (MCDM). Accordingly, mHealth can connect directly with the servers of distributed hospitals to ascertain available healthcare services for the risk-level package in those hospitals. The time of arrival of the patient at the hospital (TAH) is considered for each hospital to reach a final decision and select the appropriate institution in case of medical center failure. This paper used two datasets. The first dataset involved 572 patients with chronic heart disease. Their triage levels were evaluated using our RLLT algorithm. The second dataset included hospital healthcare services with two levels of availability within distributed hospitals to show variety when testing the proposed framework. The former dataset is an actual dataset of services collected from 12 hospitals located in the capital Baghdad, which represents the maximum level of availability. The latter is an assumption dataset of the services within the 12 hospitals located in the capital Kuala Lumpur, which represents the minimum level of availability. Subsequently, the hospitals were prioritized using a unique MCDM method for estimating small power consumption, namely, the analytic hierarchy process (AHP), based on a crossover between the ‘‘healthcare services package/TAH’’ of each hospital and the ‘‘hospital list’’. The results showed that the AHP is effective for solving hospital selection problems within mHealth. The implications of this study support the patients, organizations, and medical staff in a modern lifestyle.


[1] A. A. Zaidan et al., ‘‘A survey on communication components for IoT-based technologies in smart homes,’’ Telecommun. Syst., vol. 69, no. 1, pp. 1–25, Sep. 2018.

[2] P. P. Ray, ‘‘Understanding the role of Internet of Things towards smart e-healthcare services,’’ Biomed. Res., vol. 28, no. 4, pp. 1604–1609, 2017.

[3] H. Çal??kan, ‘‘Selection of boron based tribological hard coatings using multi-criteria decision making methods,’’ Mater. Des., vol. 50, pp. 742–749, Sep. 2013.

[4] M. Talal 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., vol. 43, no. 3, p. 42, Mar. 2019.

[5] P. P. Ray, ‘‘Home health hub Internet of Things (H3 IoT): An architectural framework for monitoring health of elderly people,’’ in Proc. Int. Conf. Sci. Eng. Manage. Res. (ICSEMR), 2014, pp. 1–3.

[6] S. V. Kovalchuk, E. Krotov, P. A. Smirnov, D. A. Nasonov, and A. N. Yakovlev, ‘‘Distributed data-driven platform for urgent decision making in cardiological ambulance control,’’ Future Gener. Comput. Syst., vol. 79, pp. 144–154, Feb. 2018.

[7] N. Kalid 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., vol. 42, no. 4, p. 69, Apr. 2018.

[8] M.-Y. Chang, C. Pang, J. M. Tarn, T.-S. Liu, and D. C. Yen, ‘‘Exploring user acceptance of an e-hospital service: An empirical study in Taiwan,’’ Comput. Standards Interfaces, vol. 38, pp. 35–43, Feb. 2015.

[9] M. V. M. Figueredo and J. S. Dias, ‘‘Mobile telemedicine system for home care and patient monitoring,’’ in Proc. 26th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 2, Sep. 2004, pp. 3387–3390.

[10] J. Wang, M. Qiu, and B. Guo, ‘‘Enabling real-time information service on telehealth system over cloud-based big data platform,’’ J. Syst. Archit., vol. 72, pp. 69–79, Jan. 2017.

[11] O. S. Albahri, A. A. Zaidan, B. B. Zaidan, M. Hashim, A. S. Albahri, and M. A. Alsalem, ‘‘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., vol. 42, no. 9, p. 164, 2018.

[12] O. S. Albahri 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., vol. 42, no. 5, p. 80, 2018.

[13] O. H. Salman, A. A. Zaidan, B. B. Zaidan, Naserkalid, and M. Hashim, ‘‘Prioritizing using ‘big data’ patients with chronic heart diseases through telemedicine environmental,’’ Int. J. Inf. Technol. Decis. Making, vol. 16, no. 5, pp. 1211–1245, 2017.

[14] S. Iqbal 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., vol. 9, no. 2, pp. 93–111, Mar. . 2019.

[15] M. Hussain et al., ‘‘Conceptual framework for the security of mobile health applications on Android platform,’’ Telematics Inform., vol. 35, no. 5, pp. 1335–1354, Mar. 2018.

[16] A. H. Mohsin, ‘‘Real-time remote health monitoring systems using body sensor information and finger vein biometric verification: A multi-layer systematic review,’’ J. Med. Syst., vol. 42, no. 12, p. 238, 2018.

[17] O. Enaizan et al., ‘‘Electronic medical record systems: Decision support examination framework for individual, security and privacy concerns using multi-perspective analysis,’’ in Health and Technology. Berlin, Germany: Springer, 2018.

[18] A. H. Mohsin et al., ‘‘Real-time medical systems based on human biometric steganography: A systematic review,’’ J. Med. Syst., vol. 42, no. 12, p. 245, 2018.

[19] M. A. Alsalem 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., vol. 42, no. 11, p. 204, 2018.

[20] D. K. Moser 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, vol. 114, no. 2, pp. 168–182, 2006.

[21] Report of the Adhoc Committee on Health Research Relating to Future Intervention Options (TDR/Gen/96.1). Investing in Health Research and Development, World Health Org., Geneva, Switzerland, 1996.

[22] O. H. Salman, M. F. A. Rasid, M. I. Saripan, and S. K. Subramaniam, ‘‘Multi-sources data fusion framework for remote triage prioritization in telehealth,’’ J. Med. Syst., vol. 38, no. 9, p. 103, Sep. 2014.

[23] R. W. Derlet, D. Kinser, L. Ray, B. Hamilton, and J. McKenzie, ‘‘Prospective identification and triage of nonemergency patients out of an emergency department: A 5-year study,’’ Ann. Emergency Med., vol. 25, no. 2, pp. 215–223, 1995.

[24] A. S. Albahri, A. A. Zaidan, O. S. Albahri, B. B. Zaidan, and M. A. Alsalem, ‘‘Real-time fault-tolerant mHealth system: Comprehensive review of healthcare services, opens issues, challenges and methodological aspects,’’ J. Med. Syst., vol. 42, no. 8, p. 137, 2018.

[25] A. Sene, B. Kamsu-Foguem, and P. Rumeau, ‘‘Telemedicine framework using case-based reasoning with evidences,’’ Comput. Methods Programs Biomed., vol. 121, no. 1, pp. 21–35, Aug. 2015.

[26] L. van Dyk, ‘‘A review of telehealth service implementation frameworks,’’ Int. J. Environ. Res. Public Health, vol. 11, no. 2, pp. 1279–1298, 2014.

[27] S. Jeong, C.-H. Youn, E. B. Shim, M. Kim, Y. M. Cho, and L. Peng, ‘‘An integrated healthcare system for personalized chronic disease care in home–hospital environments,’’ IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 4, pp. 572–585, Jul. 2012.

[28] J. L. B. Cineros and O. Lund, ‘‘KmerFinderJS: A client-server method for fast species typing of bacteria over slow Internet connections,’’ BioRxiv, p. 145284, 2017. doi: 10.1101/145284.

[29] T. Duong-Ba, T. Nguyen, B. Bose, and D. A. Tran, ‘‘Distributed client-server assignment for online social network applications,’’ IEEE Trans. Emerg. Topics Comput., vol. 2, no. 4, pp. 422–435, Dec. 2014.

[30] A. Wood, ‘‘Predicting client/server availability,’’ Computer, vol. 28, no. 4, pp. 41–48, Apr. 1995.

[31] M. L. Shuwandy, B. B. Zaidan, A. A. Zaidan, and A. S. Albahri, ‘‘Sensorbased mhealth authentication for real-time remote healthcare monitoring system: A multilayer systematic review,’’ J. Med. Syst., vol. 43, no. 2, p. 33, Feb. 2019.

[32] A. H. Mohsin et al., ‘‘Blockchain authentication of network applications: Taxonomy, classification, capabilities, open challenges, motivations, recommendations and future directions,’’ Comput. Standards Interfaces, vol. 64, pp. 41–60, May 2019.

[33] J. Dong and G.-H. Yang, ‘‘Reliable state feedback control of T–S fuzzy systems with sensor faults,’’ IEEE Trans. Fuzzy Syst., vol. 23, no. 2, pp. 421–433, Apr. 2015.

[34] Y. Xiao and H. Chen, Mobile Telemedicine: A Computing and Networking Perspective. New York, NY, USA: Auerbach Publications, 2008.

[35] B. Randell, P. Lee, and P. C. Treleaven, ‘‘Reliability issues in computing system design,’’ ACM Comput. Surv., vol. 10, no. 2, pp. 123–165, 1978.

[36] S. Murtaza and R. Al, ‘‘Qos taxonomy towards wireless body area network solutions,’’ Int. J. Appl. Innov. Eng. Manage., vol. 2, no. 4, pp. 221–234, 2013.

[37] A. Lounis, A. Hadjidj, A. Bouabdallah, and Y. Challal, ‘‘Healing on the cloud: Secure cloud architecture for medical wireless sensor networks,’’ Future Gener. Comput. Syst., vol. 55, pp. 266–277, Feb. 2016.

[38] P. Pawar, V. Jones, B.-J. F. van Beijnum, and H. Hermens, ‘‘A framework for the comparison of mobile patient monitoring systems,’’ J. Biomed. Inform., vol. 45, no. 3, pp. 544–556, Jun. 2012.

[39] A. A. Rezaee, M. H. Yaghmaee, and A. M. Rahmani, ‘‘Optimized congestion management protocol for healthcare wireless sensor networks,’’ Wireless Pers. Commun., vol. 75, no. 1, pp. 11–34, Mar. 2014.

[40] S. Ghanavati, J. Abawaji, and D. Izadi, ‘‘A congestion control scheme based on fuzzy logic in wireless body area networks,’’ in Proc. IEEE 14th Int. Symp. Netw. Comput. Appl., Sep. 2015, pp. 235–242.

[41] A. A. Rezaee, M. H. Yaghmaee, A. M. Rahmani, and A. H. Mohajerzadeh, ‘‘HOCA: Healthcare aware optimized congestion avoidance and control protocol for wireless sensor networks,’’ J. Netw. Comput. Appl., vol. 37, no. 1, pp. 216–228, Jan. 2014.

[42] K. Saleem, A. Derhab, J. Al-Muhtadi, and B. Shahzad, ‘‘Humanoriented design of secure machine-to-machine communication system for e-healthcare society,’’ Comput. Hum. Behav., vol. 51, pp. 977–985, Oct. 2015.

[43] M. Usman, M. R. Asghar, I. S. Ansari, and M. Qaraqe, ‘‘Security in wireless body area networks: From in-body to off-body communications,’’ IEEE Access, vol. 6, pp. 58064–58074, 2018.

[44] S. Moreno, A. Quintero, C. Ochoa, M. Bonfante, R. Villareal, and J. Pestana, ‘‘Remote monitoring system of vital signs for triage and detection of anomalous patient states in the emergency room,’’ in Proc. 21st Symp. Signal Process., Images Artif. Vis. (STSIVA), Aug./Sep. 2016, pp. 1–5.

[45] J. R. B. dos Santos, G. Blard, A. S. R. Oliveira, and N. B. de Carvalho, ‘‘Wireless sensor tag and network for improved clinical triage,’’ in Proc. Euromicro Conf. Digit. Syst. Design, vol. 31, 2015, pp. 399–406.

[46] J. Gómez, B. Oviedo, and E. Zhuma, ‘‘Patient monitoring system based on Internet of Things,’’ Procedia Comput. Sci., vol. 83, pp. 90–97, Dec. 2016.

[47] A. Hussain, R. Wenbi, A. L. da Silva, M. Nadher, and M. Mudhish, ‘‘Health and emergency-care platform for the elderly and disabled people in the smart city,’’ J. Syst. Softw., vol. 110, pp. 253–263, Dec. 2015.

[48] W.-T. Sung and K.-Y. Chang, ‘‘Health parameter monitoring via a novel wireless system,’’ Appl. Soft Comput., vol. 22, pp. 667–680, Sep. 2014.

[49] S. V. Zanjal and G. R. Talmale, ‘‘Medicine reminder and monitoring system for secure health using IOT,’’ Procedia Comput. Sci., vol. 78, pp. 471–476, Dec. 2016.

[50] J. Mendes, H. Simões, P. Rosa, N. Costa, C. Rabadão, and A. Pereira, ‘‘Secure low-cost solution for elder’s eCardio surveillance,’’ Procedia Comput. Sci., vol. 27, pp. 46–56, Jan. 2014.

[51] G. C. Lamprinakos et al., ‘‘An integrated remote monitoring platform towards telehealth and telecare services interoperability,’’ Inf. Sci., vol. 308, pp. 23–37, Mar. 2015.

[52] A. Ahmed, A. Rebeiro-Hargrave, Y. Nohara, E. Kai, Z. H. Ripon, and N. Nakashima, ‘‘Targeting morbidity in unreached communities using portable health clinic system,’’ in IEICE Trans. Commun., vol. E97-B, no. 3, pp. 540–545, 2014.

[53] K. Ganapathy, B. Priya, B. Priya, Dhivya, V. Prashanth, and V. Vaidehi, ‘‘SOA framework for geriatric remote health care using wireless sensor network,’’ Procedia Comput. Sci., vol. 19, pp. 1012–1019, 2013.

[54] S. J. Miah, J. Hasan, and J. G. Gammack, ‘‘On-cloud healthcare clinic: An e-health consultancy approach for remote communities in a developing country,’’ Telematics Inform., vol. 34, no. 1, pp. 311–322, 2017.

[55] G. Traverso et al., ‘‘Physiologic status monitoring via the gastrointestinal tract,’’ PLoS ONE, vol. 10, no. 11, 2015, Art. no. e0141666.

[56] R. Rajkumar and N. C. S. N. Iyengar, ‘‘Dynamic integration of mobile JXTA with cloud computing for emergency rural public health care,’’ Osong Public Health Res. Perspect., vol. 4, no. 5, pp. 255–264, 2013.

[57] N. Kumar, K. Kaur, A. Jindal, and J. J. P. C. Rodrigues, ‘‘Providing healthcare services on-the-fly using multi-player cooperation game theory in Internet of Vehicles (IoV) environment,’’ Digit. Commun. Netw., vol. 1, no. 3, pp. 191–203, Aug. 2015.

[58] S. Moretti, S. Cicalò, M. Mazzotti, V. Tralli, and M. Chiani, ‘‘Content/context-aware multiple camera selection and video adaptation for the support of m-Health services,’’ Procedia Comput. Sci., vol. 40, pp. 206–213, Jan. 2014.

[59] P. Calyam et al., ‘‘Synchronous big data analytics for personalized and remote physical therapy,’’ Pervasive Mobile Comput., vol. 28, pp. 3–20, Jun. 2016.

[60] A. Katib, D. Rao, P. Rao, K. Williams, and J. Grant, ‘‘A prototype of a novel cell phone application for tracking the vaccination coverage of children in rural communities,’’ Comput. Methods Programs Biomed., vol. 122, no. 2, pp. 215–228, Nov. 2015.

[61] O. Boursalie, R. Samavi, and T. E. Doyle, ‘‘M4CVD: Mobile machine learning model for monitoring cardiovascular disease,’’ Procedia Comput. Sci., vol. 63, no. 2, pp. 384–391, 2015.

[62] M. Fezari, R. Rasras, and I. M. M. El Emary, ‘‘Ambulatory health monitoring system using wireless sensors node,’’ Procedia Comput. Sci., vol. 65, pp. 86–94, Jan. 2015.

[63] V. Villarreal, J. Fontecha, R. Hervas, and J. Bravo, ‘‘Mobile and ubiquitous architecture for the medical control of chronic diseases through the use of intelligent devices: Using the architecture for patients with diabetes,’’ Future Gener. Comput. Syst., vol. 34, pp. 161–175, May 2014.

[64] M. Sebillo, G. Tortora, M. Tucci, G. Vitiello, A. Ginige, and P. Di Giovanni, ‘‘Combining personal diaries with territorial intelligence to empower diabetic patients,’’ J. Vis. Lang. Comput., vol. 29, pp. 1–14, Aug. 2015.

[65] M. O. Lwin et al., ‘‘A 21st century approach to tackling dengue: Crowdsourced surveillance, predictive mapping and tailored communication,’’ Acta Tropica, vol. 130, no. 1, pp. 100–107, Feb. 2014.

[66] A. Bresó, J. Martínez-Miranda, E. Fuster-García, and J. M. GarcíaGómez, ‘‘A novel approach to improve the planning of adaptive and interactive sessions for the treatment of Major Depression,’’ Int. J. Hum.- Comput. Stud., vol. 87, pp. 80–91, Mar. 2015.

[67] H. Hermens, H. op den Akker, M. Tabak, J. Wijsman, and M. Vollenbroek, ‘‘Personalized coaching systems to support healthy behavior in people with chronic conditions,’’ J. Electromyogr. Kinesiol., vol. 24, no. 6, pp. 815–826, Dec. 2014.

[68] J. Broach et al., ‘‘Usability and reliability of smart glasses for secondary triage during mass casualty incidents,’’ in Proc. Annu. Hawaii Int. Conf. Syst. Sci., 2018, pp. 1416–1422.

[69] A. Paulus, P. Meisen, T. Meisen, S. Jeschke, M. Czaplik, and F. Hirsch, ‘‘AUDIME: Augmented disaster medicine,’’ in Proc. 17th Int. Conf. E-Health Netw., Appl. Services (HealthCom), 2016, pp. 342–345.

[70] C. Beck and J. Georgiou, ‘‘A wearable, multimodal, vitals acquisition unit for intelligent field triage,’’ in Proc. IEEE Int. Symp. Circuits Syst., no. 3, May 2016, pp. 1530–1533.

[71] L. I. Besaleva and A. C. Weaver, ‘‘CrowdHelp: M-Health application for emergency response improvement through crowdsourced and sensordetected information,’’ in Proc. Wireless Telecommun. Symp., 2014, pp. 1–5.

[72] A. Ganz, J. M. Schafer, J. Tang, Z. Yang, J. Yi, and G. Ciottone, ‘‘Urbansearch and rescue situational awareness using DIORAMA disaster management system,’’ Procedia Eng., vol. 107, pp. 349–356, Jan. 2015.

[73] A. Renner et al., ‘‘RIPPLE: Scalable medical telemetry system for supporting combat rescue,’’ in Proc. IEEE Nat. Aerosp. Electron. Conf.,Jun. 2014, pp. 228–232.

[74] S. Gunasekaran and M. Suresh, ‘‘A novel control of disaster protection (NCDP) for pilgrims by pan technology,’’ in Proc. IEEE 8th Int. Conf. Intell. Syst. Control (ISCO), Jan. 2014, pp. 103–107.

[75] S. Adibi, ‘‘A mobile health network disaster management system,’’ in Proc. 7th Int. Conf. Ubiquitous Future Netw., Jul. 2015, pp. 424–428.

[76] S. Sneha and U. Varshney, ‘‘A framework for enabling patient monitoring via mobile ad hoc network,’’ Decis. Support Syst., vol. 55, no. 1, pp. 218–234, Apr. 2013.

[77] A. Fratini and M. Caleffi, ‘‘Medical emergency alarm dissemination in urban environments,’’ Telematics Inform., vol. 31, no. 3, pp. 511–517, Aug. 2014.

[78] Y. Qin, L. Li, X. Zhong, Y. Yang, and C. L. Gwee, ‘‘A cross-layer QoS design with energy and traffic balance aware for different types of traffic in MANETs,’’ Wireless Pers. Commun., vol. 85, no. 3, pp. 1429–1449, 2015.

[79] S. Manfredi, ‘‘Congestion control for differentiated healthcare service delivery in emerging heterogeneous wireless body area networks,’’ IEEE Wireless Commun., vol. 21, no. 2, pp. 80–90, Apr. 2014.

[80] J. Soto, S. Queiroz, and M. Nogueira, ‘‘Managing sensing and cooperation to analyze PUE attacks in cognitive radio ad hoc networks,’’ in Proc. 8th Int. Conf. Netw. Service Manage. (CNSM) Workshop Syst. Virtualiztion Manage. (SVM), 2012, pp. 219–223.

[81] H. F. Lingsma et al., ‘‘Comparing and ranking hospitals based on outcome: Results from The Netherlands stroke survey,’’ Int. J. Med., vol. 103, no. 2, pp. 99–108, 2009.

[82] J. Leister and J. Stausberg, ‘‘Why do patients select a hospital?’’

J. Hospital Marketing Public Relations, vol. 17, no. 2, pp. 13–31, Oct. 2007.

[83] L. H. Wizig, ‘‘Method and system for providing a user-selected healthcare services package and healthcare services panel customized based on a user’s selections,’’ U.S. Patents 6 735 569 B1, May 11, 2004.

[84] R. G. Robbinson, K. L. Kudos, L. R. Starr, K. Rao, and T. R. Price, ‘‘Mood disorders in stroke patients. Importance of lesion location.,’’ Brain, vol. 107, pp. 81–93, Mar. 1984.

[85] W. G. Barsan et al., ‘‘Time of hospital presentation in patients with acute stroke,’’ Arch. Internal Med., vol. 153, no. 22, pp. 2558–2561, 1993.

[86] J. Faulin, A. A. Juan, S. E. Grasman, and M. J. Fry, Decision Making in Service Industries: A Practical Approach. Boca Raton, FL, USA: CRC Press, 2012.

[87] Q. M. Yas, A. A. Zaidan, B. B. Zaidan, B. Rahmatullah, and H. A. Karim, ‘‘Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions,’’ Measurement, vol. 114, pp. 243–260, Jan. 2018.

[88] O. Zughoul et al., ‘‘Comprehensive insights into the criteria of student performance in various educational domains,’’ IEEE Access, vol. 6, pp. 73245–73264, 2018.

[89] N. F. Berglas, M. F. Battistelli, W. K. Nicholson, M. Sobota, R. D. Urman, and S. C. M. Roberts, ‘‘The effect of facility characteristics on patient safety, patient experience, and service availability for procedures in nonhospital-affiliated outpatient settings: A systematic review,’’ PLoS ONE, vol. 13, no. 1, 2018, Art. no. e0190975.

[90] J. Nicholl, J. West, S. Goodacre, and J. Turner, ‘‘The relationship between distance to hospital and patient mortality in emergencies: An observational study,’’ Emergency Med. J., vol. 24, no. 9, pp. 665–668, Sep. 2007.

[91] L. Wei et al., ‘‘Impact on mortality following first acute myocardial infarction of distance between home and hospital: Cohort study,’’ Heart, vol. 94, no. 9, pp. 1141–1146, 2008.

[92] R. Busse, J. Schreyögg, and P. C. Smith, ‘‘Variability in healthcare treatment costs amongst nine eu countries—Results from the healthbasket project,’’ Health Econ., vol. 17, pp. S1–S8, Jan. 2008.

[93] D. H. Akdag, T. Kalayc?, S. Karagöz, H. Zülfikar, and D. Giz, ‘‘The evaluation of hospital service quality by fuzzy MCDM,’’ Appl. Soft Comput., vol. 23, pp. 239–248, Oct. 2014.

[94] A. M. R. Khan, P. N. Prasad, and S. Rajamanoharane, ‘‘A decision-making framework for service quality measurements in hospitals,’’ Int. J. Enterprise Netw. Manage., vol. 4, no. 1, p. 80, 2010.

[95] R. L. Keeney and H. Raiffa, Decisions with Multiple Objectives: Preferences and Value Trade-Offs. Cambridge, U.K.: Cambridge Univ. Press, 1976.

[96] B. Valerie and T. J. Stewart, Multiple Criteria Decision Analysis: An Integrated Approach. Boston, MA, USA: Kluwer, 2002.

[97] M. Whaiduzzaman, A. Gani, N. B. Anuar, M. Shiraz, M. N. Haque, and I. T. Haque, ‘‘Cloud service selection using multicriteria decision analysis,’’ Sci. World J., vol. 2014, Feb. 2014, Art. no. 459375.

[98] A. A. Zaidan, B. B. Zaidan, Z. Kadhem, M. Larbani, M. B. Lakulu, and M. Hashim, ‘‘Challenges, alternatives, and paths to sustainability: Better public health promotion using social networking pages as key tools,’’ J. Med. Syst., vol. 39, no. 2, p. 7, Feb. 2015.

[99] B. B. Zaidan, A. Haiqi, A. A. Zaidan, M. Abdulnabi, M. L. M. Kiah, and H. Muzamel, ‘‘A security framework for nationwide health information exchange based on telehealth strategy,’’ J. Med. Syst., vol. 39, no. 5, p. 51, May 2015.

[100] M. L. M. Kiah, M. S. Nabi, B. B. Zaidan, and A. A. Zaidan, ‘‘An enhanced security solution for electronic medical records based on AES hybrid technique with SOAP/XML and SHA-1,’’ J. Med. Syst., vol. 37, no. 5, p. 9971, Oct. 2013.

[101] M. S. Nabi, M. L. Mat Kiah, A. A. Zaidan, and B. B. Zaidan, ‘‘Suitability of adopting S/MIME and OpenPGP email messages protocol to secure electronic medical records,’’ in Proc. 2nd Int. Conf. Future Gener. Commun. Technol. (FGCT), 2013, pp. 93–97.

[102] M. L. M. Kiah, A. Haiqi, B. B. Zaidan, and A. A. Zaidan, ‘‘Open source EMR software: Profiling, insights and hands-on analysis,’’ Comput. Methods Programs Biomed., vol. 117, no. 2, pp. 360–382, 2014.

[103] C.-L. Hwang and K. Yoon, Multiple Attribute Decision Methods and Applications. Springer Science & Business Media, 2011.

[104] B. B. Zaidan, A. A. Zaidan, and M. L. M. Kiah, ‘‘Impact of data privacy and confidentiality on developing telemedicine applications: A review participates opinion and expert concerns,’’Int. J. Pharmacol., vol. 7, no. 3, pp. 382–387, 2011.

[105] M. L. M. Kiah, B. B. Zaidan, A. A. Zaidan, M. Nabi, and R. Ibraheem, ‘‘MIRASS: Medical informatics research activity support system using information mashup network,’’ J. Med. Syst., vol. 38, no. 4, p. 37, Apr. 2014.

[106] M. L. M. Kiah, S. H. Al-Bakri, A. A. Zaidan, B. B. Zaidan, and M. Hussain, ‘‘Design and develop a video conferencing framework for real-time telemedicine applications using secure group-based communication architecture,’’ J. Med. Syst., vol. 38, no. 10, p. 133, Oct. 2014.

[107] E. Triantaphyllou, Multi-criteria Decision Making Methods: A Comparative Study, vol. 44. Boston, MA, USA: Springer, 2000, pp. 5–12.

[108] A. A. Zaidan, ‘‘A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: Coherent taxonomy, open issues and recommendation pathway solution,’’ Health Technol., vol. 8, no. 4, pp. 223–238, Sep. 2018.

[109] B. B. Zaidan, A. A. Zaidan, H. A. Karim, and N. N. Ahmad, ‘‘A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques,’’ Int. J. Inf. Technol. Decis. Making, vol. 16, pp. 1– 42, Mar. 2017.

[110] F. M. Jumaah, A. A. Zaidan, B. B. Zaidan, R. Bahbibi, M. Y. Qahtan, and A. Sali, ‘‘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., vol. 68, no. 3, pp. 425–443, Jul. 2018.

[111] B. Rahmatullah, A. A. Zaidan, F. Mohamed, and A. Sali, ‘‘Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection,’’ in Proc. 4th Int. Conf. Control, Decis. Inf. Technol. (CoDIT), 2017, pp. 1084–1088.

[112] E. Triantaphyllou, B. Shu, S. N. Sanchez, and T. Ray, ‘‘Multi-criteria decision making: An operations research approach,’’ Encyclopedia Elect. Electron. Eng., vol. 15, no. 1998, pp. 175–186, 1998.

[113] K. Yoon and C.-L. Hwang, Multiple Attribute Decision Making: An Introduction, vol. 104. Thousand Oaks, CA, USA: Sage Publications, 1995.

[114] A. A. Zaidan, B. B Zaidan, A. Al-Haiqi, M. L. M. Kiah, M. Hussain, and M. Abdulnabi, ‘‘Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS,’’ J. Biomed. Inform., vol. 53, pp. 390–404, Feb. 2015.

[115] H.-S. Shih, H.-J. Shyur, and E. S. Lee, ‘‘An extension of TOPSIS for group decision making,’’ Math. Comput. Model., vol. 45, nos. 7–8, pp. 801–813, 2007.

[116] T. L. Saaty and M. S. Ozdemir, ‘‘Why the magic number seven plus or minus two,’’ Math. Comput. Model., vol. 38, nos. 3–4, pp. 233–244, 2003.

[117] D. Lesmes, M. Castillo, and R. Zarama, ‘‘Application of the analytic network process (ANP) to establish weights in order to re-accredit a program of a University,’’ in Proc. 10th Int. Symp. Analytic Hierarchy Process, vol. 29, 2009, pp. 1–14.

[118] L. Pecchia, P. A. Bath, N. Pendleton, and M. Bracale, ‘‘Analytic hierarchy process (AHP) for examining healthcare professionals’ assessments of risk factors,’’ Methods Inf. Med., vol. 50, no. 5, pp. 435–444, Dec. 2011.

[119] M. Aruldoss, T. M. Lakshmi, and V. P. Venkatesan, ‘‘A survey on multi criteria decision making methods and its applications,’’ Amer. J. Inf. Syst., vol. 1, no. 1, pp. 31–43, 2013.

[120] N. H. Zardari, K. Ahmed, S. M. Shirazi, and Z. Bin Yusop, ‘‘Weighting methods and their effects on multi-criteria decision making model outcomes in water resources management,’’ Int. J. Oper. Res., vol. 10, no. 2, pp. 56–66, 2015.

[121] M. A. O. Barrios, F. De Felice, K. P. Negrete, B. A. Romero, A. Y. Arenas, and A. Petrillo, ‘‘An AHP-topsis integrated model for selecting the most appropriate tomography equipment,’’ Int. J. Inf. Technol. Decis. Making, vol. 15, no. 4, pp. 861–885, 2016.

[122] M. A. Ortiz, J. P. Cómbita, A. D. L. La Hoz, F. De Felice, and A. Petrillo, ‘‘An integrated approach of AHP-DEMATEL methods applied for the selection of allied hospitals in outpatient service,’’ Int. J. Med. Eng. Inform., vol. 8, no. 2, pp. 87–107, 2016.

[123] H. O. Alanazi, A. A. Zaidan, B. B. Zaidan, M. L. M. Kiah, and S. H. Al-Bakri, ‘‘Meeting the security requirements of electronic medical records in the ERA of high-speed computing,’’ J. Med. Syst., vol. 39, no. 1, p. 165, 2015.

[124] Q. M. Yas, A. A. Zaidan, B. B. Zaidan, M. Hashim, and C. K. Lim, ‘A systematic review on smartphone skin cancer apps: Coherent taxonomy, motivations, open challenges and recommendations, and new research direction,’’ J. Circuits, Syst. Comput., vol. 27, no. 5, May 2018, Art. no. 1830003.

[125] M. Abdulnabi, A. Al-Haiqi, M. L. M. Kiah, A. A. Zaidan, B. B. Zaidan, and M. Hussain, ‘‘A distributed framework for health information exchange using smartphone technologies,’’ J. Biomed. Inform., vol. 69,pp. 230–250, May 2017.

[126] World Health Organization. (2016). Health Resources Availability Mapping System (HeRAMS). Manila, Philippines: WHO Regional Office for the Western Pacific. [Online]. Available:

[127] M.-J. Lee and C.-W. Chung, ‘‘A user similarity calculation based on the location for social network services,’’ in Database Systems for Advanced Applications (Lecture Notes in Computer Science), vol. 6587. Berlin, Germany: Springer, 2011, pp. 38–52.

[128] M. Lahby, L. Cherkaoui, and A. Adib, ‘‘A novel ranking algorithm based network selection for heterogeneous wireless access,’’ J. Netw., vol. 8, no. 2, pp. 263–272, 2013.

[129] K. Saksrisathaporn, A. Bouras, N. Reeveerakul, and A. Charles, ‘‘Application of a decision model by using an integration of AHP and TOPSIS approaches within humanitarian operation life cycle,’’ Int. J. Inf. Technol. Decis. Making, vol. 15, no. 4, pp. 887–918, 2016.

[130] A. A. Zaidan, B. B. Zaidan, M. Hussain, A. Haiqi, M. L. M. Kiah, and M. Abdulnabi, ‘‘Multi-criteria analysis for OS-EMR software selection problem: A comparative study,’’ Decis. Support Syst., vol. 78, pp. 15–27, Oct. 2015.

[131] B. B. Zaidan and A. A. Zaidan, ‘‘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., vol. 26, no. 7, Jul. 2017, Art. no. 1750116.

[132] M. Hussain et al., ‘‘A security framework for mHealth apps on Android platform,’’ Comput. Secur., vol. 75, pp. 191–217, Jun. 2018.

[133] H. O. Alanazi, G. M. Alam, B. B. Zaidan, and A. A. Zaidan, ‘‘Securing electronic medical records transmissions over unsecured communications: An overview for better medical governance,’’ J. Med. Plants Res., vol. 4, no. 19, pp. 2059–2074, 2010.

[134] M. S. A. Nabi, M. L. M. Kiah, B. B. Zaidan, A. A. Zaidan, and G. M. Alam, ‘‘Suitability of using SOAP protocol to secure electronic medical record databases transmission,’’ Int. J. Pharmacol., vol. 6, no. 6, pp. 959–964, 2010.

[135] Y. Wind and T. L. Saaty, ‘‘Marketing applications of the analytic hierarchy process,’’ Manage. Sci., vol. 26, no. 7, pp. 641–658, 1980.

[136] T. L. Saaty, ‘‘A scaling method for priorities in hierarchical structures,’’ J. Math. Psychol., vol. 15, no. 3, pp. 213–281, 1977.

[137] B. N. Abdullateef, N. F. Elias, H. Mohamed, A. A. Zaidan, and B. B. Zaidan, ‘‘An evaluation and selection problems of OSS-LMS packages,’’ SpringerPlus, vol. 5, no. 1, p. 248, Dec. 2016.

[138] Q. M. Yas, A. A. Zadain, B. B. Zaidan, M. B. Lakulu, and B. Rahmatullah, ‘‘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., vol. 31, no. 3, Mar. 2017, Art. no. 1759002.

[139] B. B. Zaidan, A. A. Zaidan, H. A. Karim, and N. N. Ahmad, ‘‘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. Exper., vol. 47, no. 10, pp. 1365–1392, Oct. 2017.

[140] H. Ahmadi, M. Nilashi, and O. Ibrahim, ‘‘Organizational decision to adopt hospital information system: An empirical investigation in the case of Malaysian public hospitals,’’ Int. J. Med. Inform., vol. 84, no. 3, pp. 166–188, 2014.

[141] I. M. Ar and A. Kurtaran, ‘‘Evaluating the relative efficiency of commercial banks in Turkey: An integrated AHP/DEA approach,’’ Int. Bus. Res., vol. 6, no. 4, p. 129, 2013.

[142] K. Lam and X. Zhao, ‘‘An application of quality function deployment to improve the quality of teaching,’’ Int. J. Qual. Rel. Manage., vol. 15, no. 4, pp. 389–413, 1998.

[143] V. Sherekar and M. Tatikonda, ‘‘Impact of factor affecting on labour productivity in construction projects by AHP method,’’ Int. J. Eng. Sci. Comput., vol. 6, no. 6, pp. 6771–6775, 2016.

[144] B. B. Zaidan and A. A. Zaidan, ‘‘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, vol. 117, pp. 277–294, May 2018.

[145] S. Kubler, J. Robert, W. Derigent, A. Voisin, and Y. Le Traon, ‘‘A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications,’’ Expert Syst. Appl., vol. 65, pp. 398–422, Dec. 2016.

[146] F. M. Jumaah, A. A. Zadain, B. B. Zaidan, A. K. Hamzah, and R. Bahbibi, ‘‘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, vol. 118, pp. 83–95, Mar. 2018.

[147] I. Tariq et al., ‘‘MOGSABAT: A metaheuristic hybrid algorithm for solving multi-objective optimisation problems,’’ Neural Computing and Applications, Oct. 2018.

[148] S.-J. Chen and C.-L. Hwang, ‘‘Fuzzy multiple attribute decision making methods,’’ in Fuzzy Multiple Attribute Decision Making. Berlin, Germany: Springer, 1992, pp. 289–486.

[149] M. A. Qader, B. B. Zaidan, A. A. Zaidan, S. K. Ali, M. A. Kamaluddin, and W. B. Radzi, ‘‘A methodology for football players selection problem based on multi-measurements criteria analysis,’’ Measurement, vol. 111, pp. 38–50, Dec. 2017.

[150] O. S. Albahri et al., ‘‘Based multiple heterogeneous wearable sensors: A smart real-time health monitoring structured for hospitals distributor,’’ IEEE Access, vol. 7, pp. 37269–37323, 2019.

[151] M. Khatari, A. A. Zaidan, B. B. Zaidan, O. S. Albahri, and M. A. Alsalem, ‘‘Multi-criteria evaluation and benchmarking for active queue management methods: Open issues, challenges and recommended pathway solutions,’’ Int. J. Inf. Technol. Decis. Making, vol. 18, 2019. doi: 10.1142/S0219622019300039.

[152] B. N. Abdullateef, N. F. Elias, H. Mohamed, A. A. Zaidan, and B. B. Zaidan, ‘‘An evaluation and selection problems of OSSLMS packages,’’ SpringerPlus, vol. 5, no. 1, p. 248, 2016.




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.

Back to previous page

Installed and configured by Bahagian Automasi, Perpustakaan Tuanku Bainun, Universiti Pendidikan Sultan Idris
If you have enquiries with this repository, kindly contact us at or Whatsapp +60163630263 (Office hours only)