UPSI Digital Repository (UDRep)
|Abstract : Universiti Pendidikan Sultan Idris|
|This paper aims to assist the administration departments of medical organisations in making the right decision on selecting a suitable multiclass classification model for acute leukaemia. In this paper, we proposed a framework that will aid these departments in evaluating, benchmarking and ranking available multiclass classification models for the selection of the best one. Medical organisations have continuously faced evaluation and benchmarking challenges in such endeavour, especially when no single model is superior. Moreover, the improper selection of multiclass classification for acute leukaemia model may be costly for medical organisations. For example, when a patient dies, one such organisation will be legally or financially sued for incidents in which the model fails to fulfil its desired outcome. With regard to evaluation and benchmarking, multiclass classification models are challenging processes due to multiple evaluation and conflicting criteria. This study structured a decision matrix (DM) based on the crossover of 2 groups of multi-evaluation criteria and 22 multiclass classification models. The matrix was then evaluated with datasets comprising 72 samples of acute leukaemia, which include 5327 gens. Subsequently, multi-criteria decision-making (MCDM)techniques are used in the benchmarking and ranking of multiclass classification models. The MCDM used techniques that include the integrated BWM and VIKOR. BWM has been applied for the weight calculations of evaluation criteria, whereas VIKOR has been used to benchmark and rank classification models. VIKOR has also been employed in two decision-making contexts: individual and group decision making and internal and external group aggregation. Results showed the following: (1) the integration of BWM and VIKOR is effective at solving the benchmarking/selection problems of multiclass classification models. (2) The ranks of classification models obtained from internal and external VIKOR group decision making were almost the same, and the best multiclass classification model based on the two was ‘Bayes. Naive Byes Updateable’ and the worst one was ‘Trees.LMT’. (3) Among the scores of groups in the objective
1. Salman, O., Zaidan, A., Zaidan, B., Naserkalid, and Hashim, M.,Novel methodology for triage and prioritizing using Bbig data^ patients with chronic heart diseases through telemedicine environmental.Int. J. Inf. Technol. Decis. Mak. 16(05):1211– 1245, 2017.
2. Kalid, N. et al., Based on real time remote health monitoring systems: A new approach for prioritization Blarge scales data^ patients with chronic heart diseases using body sensors and communication technology.J. Med. Syst. 42(4):69, 2018.
3. Mohsin, A. H. et al., Based medical systems for patient’s authentication: Towards a new verification secure framework using CIA standard.J. Med. Syst. 43(7):192, 2019.
4. Mohsin, A. H. et al., Real-time medical systems based on human biometric steganography: A systematic review.J. Med. Syst. 42(12):245, 2018.
5. Mohsin, A. H. et al., Real-time remote health monitoring systems using body sensor information and finger vein biometric verification: A multi-layer systematic review.J. Med. Syst. 42(12):238, 2018.
6. Albahri, O. S. et al., Systematic review of real-time remote health monitoring system in triage and priority-based sensor technology: Taxonomy, open challenges, motivation and recommendations.J. Med. Syst. 42(5), 2018.
7. Abdulnabi, M. et al., A distributed framework for health information exchange using smartphone technologies.J. Biomed. Inform. 69:230–250, 2017.
8. Zaidan, A. A. et al., Challenges, alternatives, and paths to sustainability: Better public health promotion using social networking pages as key tools.J. Med. Syst. 39(2):7, 2015.
9. Mat Kiah, M. L. et al., Design and develop a video conferencing framework for real-time telemedicine applications using secure group-based communication architecture.J. Med. Syst. 38(10):133, 2014.
10. Shuwandy, M. L. et al., Sensor-based mHealth authentication for real-time remote healthcare monitoring system: A multilayer systematic review.J. Med. Syst. 43(2):33, 2019.
11. Talal, M. et al., Smart home-based IoT for real-time and secure remote health monitoring of triage and priority system using body sensors: Multi-driven systematic review.J. Med. Syst. 43(3):42, 2019.
12. Zaidan, B. B. et al., A security framework for Nationwide health information exchange based on telehealth strategy.J. Med. Syst. 39(5):51, 2015.
13. Hussain, M. et al., The landscape of research on smartphone medical apps: Coherent taxonomy, motivations, open challenges and recommendations.Comput. Methods Prog. Biomed. 122(3):393–408, 2015.
14. Zaidan, B. B. et al., Impact of data privacy and confidentiality on developing telemedicine applications: A review participates opinion and expert concerns.Int. J. Pharmacol. 7(3):382–387, 2011.
15. Kiah, M. L. M. et al., MIRASS: Medical informatics research activity support system using information mashup network. J. Med. Syst. 38(4):37, 2014.
16. Mohsin, A. H. et al., Based Blockchain-PSO-AES techniques in finger vein biometrics: A novel verification secure framework for patient authentication.Comput. Stand. Interfaces, 2019.
17. Hussain, M. et al., Conceptual framework for the security of mobile health applications on android platform.Telematics Inform. 35(5):1335, 2018.
18. Hussain, M. et al., A security framework for mHealth apps on android platform.Comput. Secur. 75:191–217, 2018
19. Iqbal, S. et al., Real-time-based E-health systems: Design and implementation of a lightweight key management protocol for securing sensitive information of patients.Health Technol. (Berl): 1–19, 2018.
20. Alanazi, H. O. et al., Meeting the security requirements of electronic medical records in the ERA of high-speed computing.J. Med. Syst. 39(1):165, 2015.
21. Nabi, M. S. A. et al., Suitability of using SOAP protocol to secure electronic medical record databases transmission.Int. J. Pharmacol. 6(6):959–964, 2010.
22. Kiah, M. L. M. et al., An enhanced security solution for electronic medical records based on AES hybrid technique with SOAP/XML and SHA-1.J. Med. Syst. 37(5):9971, 2013.
23. Nabi, M. S. et al., Suitability of adopting S/MIME and OpenPGP email messages protocol to secure electronic medical records. In: Second International Conference on Future Generation Communication Technologies (FGCT 2013), 2013, 93–97.
24. Kiah, M. L. M. et al., Open source EMR software: Profiling, insights and hands-on analysis.Comput. Methods Prog. Biomed. 117(2):360–382, 2014.
25. Alsalem, M. A. 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 Prog. Biomed. 158:93–112, 2018.
26. Srisukkham, W., Zhang, L., Neoh, S. C., Todryk, S., and Lim, C. P., Intelligent leukaemia diagnosis with bare-bones PSO based feature optimization.Appl. Soft Comput. 56:405–419, 2017.
27. Labati, R. D., Piuri, V., Scotti, F., and Ieee, All-IDB: The acute lymphoblastic leukemia image database for image processing. In: 2011 18th Ieee International Conference on Image Processing, 2011.
28. Lei, X., and Chen, Y., Multiclass classification of microarray data samples with flexible neural tree. In:2012 Spring Congress on Engineering and Technology, 2012, 1–4.
29. Agaian, S., Madhukar, M., and Chronopoulos, A. T., Automated screening system for acute myelogenous leukemia detection inblood microscopic images.IEEE Syst. J. 8:995–1004, 2014.
30. Mohapatra, S., Patra, D., and Satpathi, S., Image analysis of blood
microscopic images for acute leukemia detection. In:2010 international Conference on Industrial Electronics, Control and Robotics, 2010, 215–219.
31. Bagasjvara, R. G., Candradewi, I., Hartati, S., and Harjoko, A., Automated detection and classification techniques of acute leukemia using image processing: A review. In:2016 2nd International Conference on Science and Technology-Computer (ICST), 2016, 35–43.
32. Rawat, J., Singh, A., Bhadauria, H. S., and Virmani, J., Computer aided diagnostic system for detection of leukemia using microscopic images.Procedia Computer Science 70:748–756, 2015.
33. Snousy, M. B. A., El-Deeb, H. M., Badran, K., and Khlil, I. A. A., Suite of decision tree-based classification algorithms on cancer gene expression data.Egyptian Informatics Journal 12:73–82, 2011.
34. Goutam, D., and Sailaja, S., Classification of acute myelogenous leukemia in blood microscopic images using supervised classifier. In:2015 IEEE International Conference on Engineering and Technology (ICETECH), 2015, 1–5.
35. Mishra, S., Majhi, B., Sa, P. K., and Sharma, L., Gray level cooccurrence matrix and random forest based acute lymphoblastic leukemia detection.Biomedical Signal Processing and Control 33: 272–280, 2017.
36. Nguyen, T., and Nahavandi, S., Modified AHP for gene selection and Cancer classification using Type-2 fuzzy logic.IEEE Trans.Fuzzy Syst. 24:273–287, 2016.
37. Hossin, M., and Sulaiman, M., A review on evaluation metrics for data classification evaluations.International Journal of Data Mining & Knowledge Management Process 5:1, 2015.
38. Sokolova, M., and Lapalme, G., A systematic analysis of performance measures for classification tasks.Inf. Process. Manag. 45: 427–437, 2009.
39. Krappe, S., Benz, M., Wittenberg, T., Haferlach, T., and Munzenmayer, C., Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: A comparison of two classification schemes with respect to the segmentation quality. In: Hadjiiski, L. M., Tourassi, G. D. (Eds),Medical Imaging 2015: Computer-Aided Diagnosis. Vol. 9414, 2015.
40. Cui, Y., Zheng, C.-H., Yang, J., and Sha, W., Sparse maximum margin discriminant analysis for feature extraction and gene selection on gene expression data.Comput. Biol. Med. 43:933–941, 2013.
41. Mohapatra, P., Chakravarty, S., and Dash, P. K., Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system.Swarm and Evolutionary Computation 28:144–160, 2016.
42. Wang, H.-Q., Wong, H.-S., Zhu, H., and Yip, T. T. C., A neural network-based biomarker association information extraction approach for cancer classification.J. Biomed. Inform. 42:654–666, 2009.
43. Zhang, L., and Xiaojuan, H., Multiple SVM-RFE for multi-class gene selection on DNA microarray data. In:2015 International Joint Conference on Neural Networks (IJCNN), 2015, 1–6.
44. Yongqiang, D., Bin, H., Yun, S., Chengsheng, M., Jing, C., Xiaowei, Z. et al., Feature selection of high-dimensional biomedical data using improved SFLA for disease diagnosis. In:2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015, 458–463.
45. Salem, H., Attiya, G., and El-Fishawy, N., Gene expression profiles based human cancer diseases classification. In:2015 11th International Computer Engineering Conference (ICENCO), 2015, 181–187.
46. Campos, L. M. d., Cano, A., Castellano, J. G., and Moral, S., Bayesian networks classifiers for gene-expression data. In:201111th International Conference on Intelligent Systems Design and
Applications, 2011, 1200–1206.
47. Bhattacharjee, R., and Saini, L. M., Detection of acute lymphoblastic leukemia using watershed transformation technique. In: 2015 International Conference on Signal Processing, Computing and Control (ISPCC), 2015, 383–386.
48. Chandra, B., and Gupta, M., Robust approach for estimating probabilities in Naïve–Bayes classifier for gene expression data.Expert Syst. Appl. 38:1293–1298, 2011.
49. Singhal, V., and Singh, P., Local binary pattern for automatic detection of acute lymphoblastic leukemia. In:2014 Twentieth National Conference on Communications (NCC), 2014, 1–5.
50. Rashid, S., and Maruf, G. M., An adaptive feature reduction algorithm for cancer classification using wavelet decomposition of serum proteomic and DNA microarray data. In:2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 2011, 305–312.
51. Ludwig, S. A., Jakobovic, D., and Picek, S., Analyzing gene expression data: Fuzzy decision tree algorithm applied to the classification of cancer data. In:2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015, 1–8.
52. Saritha, M., Prakash, B. B., Sukesh, K., and Shrinivas, B., Detection of blood cancer in microscopic images of human blood samples: A review. In:2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016, 596–600.
53. Tai, W. L., Hu, R. M., Hsiao, H. C. W., Chen, R. M., and Tsai, J. J. P., Blood cell image classification based on hierarchical SVM. In:2011 IEEE International Symposium on Multimedia, 2011, 129–136.
54. Kumar, P. G., Aruldoss Albert Victoire, T., Renukadevi, P., and Devaraj, D., Design of fuzzy expert system for microarray data classification using a novel genetic swarm algorithm.Expert Syst. Appl. 39:1811–1821, 2012.
55. He, Y., and Hui, S. C., Exploring ant-based algorithms for gene expression data analysis.Artif. Intell. Med. 47:105–119, 2009.
56. Yusen, Z., and Liangyun, R., Two feature selections for analysis of microarray data. In:2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010, 1259–1262.
57. Rosa, J. L. D., Magpantay, A. E. A., Gonzaga, A. C., and Solano, G. A., Cluster center genes as candidate biomarkers for the classification of leukemia. In:IISA 2014, the 5th International Conference on Information, Intelligence, Systems and Applications, 2014, 124–129.
58. Lu, X., Peng, X., Liu, P., Deng, Y., Feng, B., and Liao, B., A novel feature selection method based on CFS in cancer recognition. In: 2012 IEEE 6th International Conference on Systems Biology (ISB), 2012, 226–231.
59. Kumar, M., and Kumar Rath, S., Classification of microarray using MapReduce based proximal support vector machine classifier.Knowl.-Based Syst. 89:584–602, 2015.
60. Dash, S., Hill-climber based fuzzy-rough feature extraction with an application to cancer classification. In:13th International Conference on Hybrid Intelligent Systems (HIS 2013), 2013, 28–34.
61. Wahbeh, A. H., Al-Radaideh, Q. A., Al-Kabi, M. N., and AlShawakfa, E. M., A comparison study between data mining tools over some classification methods.Int. J. Adv. Comput. Sci. Appl. Special Issue on Artificial Intelligence:18–26, 2011.
62. Rangra, K., and Bansal, D. K. L., Comparative study of data mining tools.International Journal of Advanced Research in Computer Science and Software Engineering 4(6), 2014.
63. Yas, Q. M., Zaidan, A. A., Zaidan, B. B., Rahmatullah, B., and Karim, H. A., Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions. Measurement 114:243– 260, 2018.
64. Wang, Z., and Palade, V., A comprehensive fuzzy-based framework for Cancer microarray data gene expression analysis. In: 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering, 2007, 1003–1010.
65. Nazlibilek, S., Karacor, D., Ercan, T., Sazli, M. H., Kalender, O., and Ege, Y., Automatic segmentation, counting, size determination and classification of white blood cells.Measurement 55:58–65, 2014.
66. Bhattacharjee, R., and Saini, L. M., Robust technique for the detection of acute lymphoblastic leukemia. In:2015 IEEE Power, Communication and Information Technology Conference (PCITC), 2015, 657–662.
67. Torkaman, A., Charkari, N. M., Aghaeipour, M., and Hajati, E., A recommender system for detection of leukemia based on cooperative game. In:2009 17th Mediterranean Conference on Control and Automation, 2009, 1126–1130.
68. Escalante, H. J., Montes-y-Gómez, M., González, J. A., GómezGil, P., Altamirano, L., Reyes, C. A. et al., Acute leukemia classification by ensemble particle swarm model selection.Artif. Intell. Med. 55:163–175, 2012.
69. Madhloom, H. T., Kareem, S. A., and Ariffin, H., A robust feature extraction and selection method for the recognition of lymphocytes versus acute lymphoblastic leukemia. In:2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), 2012, 330–335.
70. Cornet, E., Perol, J. P., and Troussard, X., Performance evaluation and relevance of the CellaVision (TM) DM96 system in routine analysis and in patients with malignant hematological diseases.Int. J. Lab. Hematol. 30:536–542, 2008.
71. Rota, P., Groeneveld-Krentz, S., and Reiter, M., On automated flow cytometric analysis for MRD estimation of acute lymphoblastic Leukaemia: A comparison among different approaches. In: 2015 IEEE International Conference on Bioinformatics andBiomedicine (BIBM), 2015, 438–441.
72. Keeney, R. L., and Raiffa, H.,Decisions with Multiple Objectives: preferences and Value Trade-Offs. Cambridge: Cambridge university press, 1993.
73. Zaidan, A., Zaidan, B., Al-Haiqi, A., Kiah, M. L. M., Hussain, M., and Abdulnabi, M., Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS.J. Biomed. Inform. 53:390–404, 2015.
74. Khatari, M. et al., Multi-criteria evaluation and benchmarking for active queue management methods: Open issues, challenges and recommended pathway solutions.Int. J. Inf. Technol. Decis. Mak.:S0219622019300039, 2019.
75. Zaidan, A. A. et al., Multi-criteria analysis for OS-EMR software selection problem: A comparative study.Decis. Support. Syst. 78:15–27, 2015.
76. Zaidan, B. B. et al., A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi-criteria analysis based on ‘large-scale data.Softw. Pract. Exp. 47(10):1365–1392, 2017.
77. Yas, Q. M. et al., Towards on develop a framework for the evaluation and benchmarking of skin detectors based on artificial intelligent models using multi-criteria decision-making techniques.Int. J. Pattern Recognit. Artif. Intell. 31(03):1759002, 2017.
78. Belton, V., and Stewart, T.,Multiple Criteria Decision Analysis: An Integrated Approach. Boston: Kluwer Academic Publishers, 2002.
79. Zaidan, B., Zaidan, A., Abdul Karim, H., and Ahmad, N., A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques.Int. J. Inf. Technol. Decis. Mak.:1–42, 2017.
80. Zaidan, B., and Zaidan, A., Software and hardware FPGA-based digital watermarking and steganography approaches: Toward new methodology for evaluation and benchmarking using multicriteria decision-making techniques.Journal of Circuits, Systems and Computers 26(07):1750116, 2017.
81. Abdullateef, B. N., Elias, N. F., Mohamed, H., Zaidan, A., and Zaidan, B., An evaluation and selection problems of OSS-LMS packages.SpringerPlus 5(1):248, 2016.
82. Qader, M. A. et al., A methodology for football players selection problem based on multi-measurements criteria analysis.Measurement 111:38–50, 2017.
83. Rahmatullah, B. et al., Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection. In:2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017. Vol. 2017, 2017, 1084– 1088.
84. Jumaah, F. M. et al., Technique for order performance by similarity to ideal solution for solving complex situations in multi-criteria optimization of the tracking channels of GPS baseband telecommunication receivers.Telecommun. Syst.:1–19, 2018.
85. Petrovic-Lazarevic, S., & Abraham, A., Hybrid fuzzy-linear programming approach for multi criteria decision making problems.Neural Parallel & Scientific Comp., 11:53-68, 2003.
86. Malczewski, J.,GIS and Multicriteria Decision Analysis. New York: Wiley, 1999.
87. Alsalem, M., Zaidan, A., Zaidan, B., Hashim, M., Albahri, O., Albahri, A. et al., Systematic review of an automated multiclass detection and classification system for acute Leukaemia in terms of evaluation and benchmarking, open challenges, issues and methodological aspects.J. Med. Syst. 42(11):204, 2018.
88. Yas, Q. M. et al., Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions.Measurement 114:243–260, 2018.
89. Zaidan, B. B., and Zaidan, A. A., Comparative study on the evaluation and benchmarking information hiding approaches based multi-measurement analysis using TOPSIS method with different normalisation, separation and context techniques.Measurement 117:277–294, 2018.
90. Zaidan, A. A. et al., A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: Coherent taxonomy, open issues and recommendation pathway solution.Health Technol. (Berl). 8(4):223–238, 2018.
91. Zionts, S., MCDM-if not a Roman numeral, then what?Interfaces 9:94–101, 1979.
92. Baltussen, R., and Niessen, L., Priority setting of health interventions: The need for multi-criteria decision analysis.Cost effectiveness and resource allocation 4:1, 2006.
93. Thokala, P., Devlin, N., Marsh, K., Baltussen, R., Boysen, M., Kalo, Z. et al., Multiple criteria decision analysis for health care decision making—An introduction: Report 1 of the ISPOR MCDA emerging good practices task force.Value Health 19:1– 13, 2016.
94. Oliveira, M., Fontes, D. B., and Pereira, T., Multicriteria decision making: A case study in the automobile industry.Annals of Management Science 3:109, 2014.
95. Tariq, I. et al., MOGSABAT: A metaheuristic hybrid algorithm for solving multi-objective optimisation problems.Neural Comput. & Applic. 30:1–15, 2018.
96. Enaizan, O. et al., Electronic medical record systems: Decision support examination framework for individual, security and privacy concerns using multi-perspective analysis.Health Technol., 1-18, 2018.
97. Salih, M. M. et al., Survey on fuzzy TOPSIS state-of-the-art between 2007–2017.Comput. Oper. Res., 104:207–227, 2019.
98. Kalid, N. et al., Based real time remote health monitoring systems: A review on patients prioritization and related" big data" using body sensors information and communication technology.J. Med. Syst. 42(2):30, 2018.
99. Jumaah, F. M. et al., Decision-making solution based multimeasurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment.Measurement 118:83–95, 2018.
100. Jadhav, A., and Sonar, R., Analytic hierarchy process (AHP), weighted scoring method (WSM), and hybrid knowledge based system (HKBS) for software selection: A comparative study. In: 2009 Second International Conference on Emerging Trends in Engineering & Technology, 2009, 991–997.
101. Albahri, A. S. et al., Real-time fault-tolerant mHealth system: Comprehensive review of healthcare services, opens issues, challenges and methodological aspects.J. Med. Syst. 42(8):137, 2018 Springer US.
102. Albahri, O. S. et al., Real-time remote health-monitoring systems in a Medical Centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects.J. Med. Syst. 42(9):164, 2018.
103. Talal, M. et al., Comprehensive review and analysis of antimalware apps for smartphones.Telecommun. Syst., 1-53, 2019.
104. Zaidan, A. A. et al., Based multi-agent learning neural network and Bayesian for real-time IoT skin detectors: A new evaluation and benchmarking methodology.Neural Comput. & Applic., 2019.
105. Albahri, A. S. et al., Based multiple heterogeneous wearable sensors: A smart real-time health monitoring structured for hospitals distributor.IEEE Access 7:37269–37323, 2019.
106. Albahri, O. S. et al., Fault-tolerant mHealth framework in the context o f IoT-based real -time wea rable health data sensors.IEEE Access 7:50052–50080, 2019.
107. Whaiduzzaman, M., Gani, A., Anuar, N. B., Shiraz, M., Haque, M. N., and Haque, I. T., Cloud service selection using multicriteria decision analysis.Sci. World J. 2014:459375, 2014.
108. Aruldoss, M., Lakshmi, T. M., and Venkatesan, V. P., A survey on multi criteria decision making methods and its applications.American Journal of Information Systems 1:31–43,2013.
109. Singh, A., & Malik, SK., Major MCDM techniques and their application-a review.IOSR Journal of Engineering, 4(5):15-25,2014.
110. Opricovic, S., and Tzeng, G.-H., Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS.Eur. J. Oper. Res. 156:445–455, 2004.
111. Guo, S., and Zhao, H., Fuzzy best-worst multi-criteria decisionmaking method and its applications.Knowl.-Based Syst. 121:23–31, 2017.
112. Rezaei, J., Best-worst multi-criteria decision-making method.Omega 53:49–57, 2015.
113. Tavana, M., and Hatami-Marbini, A., A group AHP-TOPSIS framework for human spaceflight mission planning at NASA.Expert Syst. Appl. 38:13588–13603, 2011.
114. Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., Albahri, A. S., Yas, Q. M. et al., A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: Coherent taxonomy, open issues and recommendation pathway solution.Heal. Technol. 8:223–238, 2018.
115. Azeez, D., Ali, M. A. M., Gan, K. B., and Saiboon, I., Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department.SpringerPlus 2:416, 2013.
116. Ashour, O. M., and Okudan, G. E., Fuzzy AHP and utility theory based patient sorting in emergency departments. International Journal of Collaborative Enterprise 1:332–358, 2010.
117. Mills, A. F., A simple yet effective decision support policy for mass-casualty triage.Eur. J. Oper. Res. 253:734–745, 2016.
118. Adunlin, G., Diaby, V., and Xiao, H., Application of multicriteria decision analysis in health care: A systematic review and bibliometric analysis.Health Expect. 18:1894–1905, 2015.
119. Jumaah, F., Zadain, A., Zaidan, B., Hamzah, A., and 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, 118:83-95, 2018.
120. Yas, Q. M., Zaidan, A., Zaidan, B., Rahmatullah, B., and Karim, H. A., Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions.Measurement, 114:243-260, 2018.
121. Nilsson, H., Nordström, E.-M., and Öhman, K., Decision support for participatory forest planning using AHP and TOPSIS.Forests 7:100, 2016.
122. Kornyshova, E., and Salinesi, C., MCDM techniques selection approaches: State of the art. In:2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, 2007, 22–29.
123. Kaya, ?., Çolak, M., and Terzi, F., Use of MCDM techniques for energy policy and decision-making problems: A review.Int. J. Energy Res. 42:2344–2372, 2018.
124. Wan Ahmad, W. N. K., Rezaei, J., Sadaghiani, S., and Tavasszy, L. A., Evaluation of the external forces affecting the sustainability of oil and gas supply chain using best worst method.J. Clean. Prod. 153:242–252, 2017.
125. Gupta, H., and Barua, M. K., Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS.J. Clean. Prod. 152:242–258, 2017.
126. Rezaei, J., Best-worst multi-criteria decision-making method: Some properties and a linear model.Omega 64:126–130, 2016.
127. Yang, Q., Zhang, Z., You, X., and Chen, T., Evaluation and classification of overseas talents in China based on the BWM for intuitionistic relations.Symmetry 8:137, 2016.
128. Opricovic, S., and Tzeng, G.-H., Extended VIKOR method in comparison with outranking methods.Eur. J. Oper. Res. 178: 514–529, 2007.
129. Mahjouri, M., Ishak, M. B., Torabian, A., Abd Manaf, L., Halimoon, N., and Ghoddusi, J., Optimal selection of Iron and steel wastewater treatment technology using integrated multicriteria decision-making techniques and fuzzy logic.Process Saf. Environ. Prot. 107:54–68, 2017.
130. Ren, J., Selection of sustainable prime mover for combined cooling, heat, and power technologies under uncertainties: An interval multicriteria decision-making approach.Int. J. Energy Res., 42(8):2655-2669, 2018.
131. Gupta, H., Evaluating service quality of airline industry using hybrid best worst method and VIKOR.J. Air Transp. Manag. 68:35–47, 2018.
132. Serrai, W., Abdelli, A., Mokdad, L., and Hammal, Y., An efficient approach for web service selection. In:2016 IEEE Symposium on Computers and Communication (ISCC), 2016, 167–172.
133. Shojaei, P., Seyed Haeri, S. A., and Mohammadi, S., Airports evaluation and ranking model using Taguchi loss function, bestworst method and VIKOR technique.J. Air Transp. Manag. 68:4– 13, 2018.
134. Serrai, W., Abdelli, A., Mokdad, L., and Hammal, Y., Towards an efficient and a more accurate web service selection using MCDM methods.J. Comput. Sci. 22:253–267, 2017.
135. Pamu?ar, D., Petrovi?, I., and ?irovi?, G., Modification of the best–worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers.Expert Syst. Appl. 91:89–106, 2018.
136. Tian, Z.-p., Wang, J.-q., and Zhang, H.-y., An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods.Appl. Soft Comput., 72:636-646, 2018.
137. Chiu, W.-Y., Tzeng, G.-H., and Li, H.-L., A new hybrid MCDM model combining DANP with VIKOR to improve e-store business.Knowl.-Based Syst. 37:48–61, 2013.
138. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P. et al., Molecular classification of cancer: Class discovery and class prediction by gene expressionmonitoring.Science 286:531–537, 1999
139. Zhou, C., Wan, L., and Liang, Y., A hybrid algorithm of minimum spanning tree and nearest neighbor for classifying human cancers. In:Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on, 2010, V5-585–V5-589.
140. Chakraborty, S., Simultaneous cancer classification and gene selection with Bayesian nearest neighbor method: An integrated approach.Computational Statistics & Data Analysis 53:1462–1474, 2009.
141. Chunbao, Z., Liming, W., and Yanchun, L., A hybrid algorithm of minimum spanning tree and nearest neighbor for classifying human cancers. In:2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), 2010, V5-585–V5-589.
142. Horng, J.-T., Wu, L.-C., Liu, B.-J., Kuo, J.-L., Kuo, W.-H., and Zhang, J.-J., An expert system to classify microarray gene expression data using gene selection by decision tree.Expert Syst. Appl. 36:9072–9081, 2009.
143. Garro, B. A., Rodríguez, K., and Vazquez, R. A., Designing artificial neural networks using differential evolution for classifying DNA microarrays. In:2017 IEEE Congress on Evolutionary Computation (CEC), 2017, 2767–2774.
144. Al-Sahaf, H., Song, A., and Zhang, M., Hybridisation of genetic programming and nearest neighbour for classification. In:2013 IEEE Congress on Evolutionary Computation, 2013, 2650–2657.
145. Deegalla, S., and Boström, H., Improving fusion of dimensionality reduction methods for nearest neighbor classification. In:2009 International Conference on Machine Learning and Applications, 2009, 771–775.
146. Hasan, A., and Akhtaruzzaman, A. M., High dimensional microarray data classification using correlation based feature selection. In:2012 International Conference on Biomedical Engineering (ICoBE), 2012, 319–321.
147. Huang, P. H., and Moh, T.-t., A non-linear non-weight method for multi-criteria decision making.Ann. Oper. Res. 248:239–251,2017.
148. Aboutorab, H., Saberi, M., Asadabadi, M. R., Hussain, O., and Chang, E., ZBWM: The Z-number extension of best worst method and its application for supplier development.Expert Syst. Appl. 107:115–125, 2018.
149. Almahdi, E. M. et al., Based mobile patient monitoring systems: A prioritization framework using multi-criteria decision making techniques. J. Med. Syst. 43, 2019.
150. Almahdi, E. M. et al., Mobile patient monitoring systems from a benchmarking aspect: Challenges, open issues and recommended solutions. J. Med. Syst. 43, 2019.
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