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Prioritizing the glucose-lowering medicines for type 2 diabetes by an extended fuzzy decision-making approach with target-based attributes

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Abstract

Different therapeutic classes have been authorized for the treatment of hyperglycemia in type 2 diabetic patients, and even more drug classes are under development. This variety of alternative treatments and the general treatment algorithms of the clinical guidelines lead to a nonuniform prescription of drugs by endocrinologists and diabetic specialists. Diabetes medication choice is a multi-objective problem with many difficulties in making rational decisions because of the wide range of hyperglycemia-lowering agents with multiple benefits and multiple risk elements. This paper proposes a group Entropy–CRiteria Importance Through Inter-criteria Correlation (CRITIC)–Weighted Aggregated Sum Product ASsessment (WASPAS) multi-criteria decision-making (MCDM) model with target-based criteria to prioritize and rank the glucose-lowering medicines for type 2 diabetes using the American Diabetes Association and International Diabetes Federation Clinical Guidelines. The proposed model consists of a weighting method comprising both objective and subjective approaches; the two most common objective approaches (i.e., Entropy and CRITIC methods) are used to find the objective weights. Then, these weights are aggregated with the subjective weights that endocrinologists assign to the criteria. Afterward, a WASPAS target-based method is developed to provide the final ranking of the medications. Finally, the close correlation between the final ranking of the proposed methodology and the average priority order of the medications obtained by different MCDM methods implies the strength and validity of the model performance.

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References

  1. International Diabetes Federation. IDF diabetes Atlas. 10th ed. Brussels, Belgium: International Diabetes Federation; 2021 [cited 2021 Dec 13]. Available from: http://www.diabetesatlas.org.

  2. American Diabetes Association (2018) Economic costs of diabetes in the US in 2017. Diabetes Care 41(5):917–928

    Article  PubMed Central  Google Scholar 

  3. Type 2 diabetes, International Diabetes Federation; 2020 [cited 2021 Nov 22]. Available from: http://www.idf.org/aboutdiabetes/type-2-diabetes.

  4. Simó R, Hernández C (2002) Treatment of diabetes mellitus: general goals, and clinical practice management. Rev Esp Cardiol 55(8):845–860

    Article  PubMed  Google Scholar 

  5. Nasli-Esfahani E, Peimani M, Rambod C, Omidvar M, Larijani B (2014) Developing a clinical diabetes guideline in diabetes research network in Iran. Iran J Public Health 43(6):713

    PubMed  PubMed Central  Google Scholar 

  6. Grant RW, Wexler DJ, Watson AJ, Lester WT, Cagliero E, Campbell EG, Nathan DM (2007) How doctors choose medications to treat type 2 diabetes: a national survey of specialists and academic generalists. Diabetes Care 30(6):1448–1453

    Article  PubMed  Google Scholar 

  7. American Diabetes Association. 9. Pharmacologic approaches to glycemic treatment: Standards of Medical Care in Diabetes—2020. Diabetes care. 2020 Jan 1;43(Supplement 1):S98–110.

  8. 2020 ADA guidelines: medications for type 2 diabetes, Tashko G; 2020 [cited 2021 Nov 22]. Available from: https://www.gertitashkomd.com/blog/2020/3/15/2020-ada-guidelines-medications-for-type-2-diabetes

  9. Montori VM (2016 Mar) Selecting the right drug treatment for adults with type 2 diabetes. BMJ 30(352):i1663. https://doi.org/10.1136/bmj.i1663

    Article  Google Scholar 

  10. Mayo clinic. Diabetes Medication Choice Decision Conversation Aid. 2020 [cited 2021 Nov 22]. Available from: https://diabetesdecisionaid.mayoclinic.org/index

  11. Dolan JG (2010) Multi-criteria clinical decision support. Patient: Patient-Centered Outcomes Res 3(4):229–248

    Article  Google Scholar 

  12. Maruthur NM, Joy SM, Dolan JG, Shihab HM, Singh S (2015) Use of the analytic hierarchy process for medication decision-making in type 2 diabetes. PLoS ONE 10(5):e0126625

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Peteiro-Barral D, Remeseiro B, Méndez R, Penedo MG (2017) Evaluation of an automatic dry eye test using MCDM methods and rank correlation. Med Biol Eng Compu 55(4):527–536

    Article  Google Scholar 

  14. Felli JC, Noel RA, Cavazzoni PA (2009) A multiattribute model for evaluating the benefit-risk profiles of treatment alternatives. Med Decis Making 29(1):104–115

    Article  PubMed  Google Scholar 

  15. Hafezalkotob A, Hafezalkotob A (2015) Comprehensive MULTIMOORA method with target-based attributes and integrated significant coefficients for materials selection in biomedical applications. Mater Des 15(87):949–959

    Article  CAS  Google Scholar 

  16. Yildirim FS, Sayan M, Sanlidag T, Uzun B, Ozsahin DU, Ozsahin I (2021) Comparative evaluation of the treatment of COVID-19 with multicriteria decision-making techniques. J Healthcare Eng 22:2021

    Google Scholar 

  17. Al-Rubeaan K, Bana FA, Alruwaily FG, Sheshah E, Alnaqeb D, AlQahtani AM, Ewais D, Al Juhani N, Hassan AH, Youssef AM (2020) Physicians’ choices in the first-and second-line management of type 2 diabetes in the Kingdom of Saudi Arabia. Saudi Pharmaceutical J 28(3):329–337

    Article  Google Scholar 

  18. Eghbali-Zarch M, Tavakkoli-Moghaddam R, Esfahanian F, Sepehri MM, Azaron A (2018) Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment. Artif Intell Med 1(87):20–33

    Article  Google Scholar 

  19. Rani P, Mishra AR, Mardani A (2020) An extended Pythagorean fuzzy complex proportional assessment approach with new entropy and score function: application in pharmacological therapy selection for type 2 diabetes. Appl Soft Comput 1(94):106441

    Article  Google Scholar 

  20. Nag K, Helal M (2018) Multicriteria Inventory Classification of Diabetes Drugs Using a Comparison of AHP and Fuzzy AHP Models. In 2018 IEEE Int Conf Industrial Eng Eng Manag (IEEM) 1456–1460. IEEE.

  21. Balubaid MA, Basheikh MA (2016) Using the analytic hierarchy process to prioritize alternative medicine: selecting the most suitable medicine for patients with diabetes. Int J Basic Appl Sci 5(1):67

    Article  CAS  Google Scholar 

  22. Chen RC, Chiu JY, Batj CT (2011) The recommendation of medicines based on multiple criteria decision making and domain ontology—an example of anti-diabetic medicines. In2011 Int Conf Machine Learn Cybernetics 1, 27–32. IEEE.

  23. Ahmed S, Roy S, Alam GR (2021) Benchmarking and Selecting Optimal Diabetic Retinopathy Detecting Machine Learning Model using Entropy and TOPSIS Method. In2021 Int Conf Electrical Comput Communications Mechatronics Eng (ICECCME) 1–6. IEEE.

  24. Chang HY, Lo CL, Chang HL (2021) Development of the benefit-risk assessment of complementary and alternative medicine use in people with diabetes: a Delphi-analytic hierarchy process approach. Computers, Informatics, Nursing: CIN.

  25. Yas QM (2021) Evaluation multi diabetes mellitus symptoms by integrated fuzzy-based MCDM approach. Turkish J Comput Math Educ (TURCOMAT) 12(13):4069–4082

    Google Scholar 

  26. Ahmadi K, Ebrahimi M (2019) A novel algorithm based on information diffusion and fuzzy MADM methods for analysis of damages caused by diabetes crisis. Appl Soft Comput 1(76):205–220

    Article  Google Scholar 

  27. Gupta K, Roy S, Poonia RC, Nayak SR, Kumar R, Alzahrani KJ, Alnfiai MM, Al-Wesabi FN (2022) Evaluating the usability of mhealth applications on type 2 diabetes mellitus using various MCDM methods. InHealthcare 10, No. 1. Multidisciplinary Digital Publishing Institute (MDPI)

  28. Wang X, He L, Zhu K, Zhang S, Xin L, Xu W, Guan Y (2019) An integrated model to evaluate the impact of social support on improving self-management of type 2 diabetes mellitus. BMC Med Inform Decis Mak 19(1):1–2

    Article  PubMed  PubMed Central  Google Scholar 

  29. Sharawat K, Dubey SK (2018) Diet Recommendation for Diabetic Patients Using MCDM Approach. In Intelligent Communication, Control Devices (pp. 239–246). Springer, Singapore.

  30. Ebrahimi M, Ahmadi K (2017 Jun 16) Diabetes-related complications severity analysis based on hybrid fuzzy multi-criteria decision making approaches. Front Health Inform 6(1):11–22

    Google Scholar 

  31. Mühlbacher AC, Bethge S, Kaczynski A, Juhnke C (2015) Objective criteria in the medicinal therapy for type II diabetes: an analysis of the patients’ perspective with analytic hierarchy process and best-worst scaling. Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)), 78(5):326–336

  32. Wang M, Liu YW, Li X (2014) Type-2 diabetes management using analytic hierarchy process and analytic network process. In Proc 11th IEEE Int Conf Networking Sens Control 655–660).IEEE.

  33. Bondor CI, Kacso IM, Lenghel A, Istrate D, Muresan A (2013) VIKOR method for diabetic nephropathy risk factors analysis. Appl Med Inform 32(1):43–52

    Google Scholar 

  34. Mehrotra S, Kim K (2011) Outcome based state budget allocation for diabetes prevention programs using multi-criteria optimization with robust weights. Health Care Manag Sci 14(4):324–337

    Article  PubMed  Google Scholar 

  35. Amin-Naseri MR, Neshat N (2011) An expert system based on analytical hierarchy process for diabetes risk assessment (DIABRA). In Int Conf Swarm Intelligence 252-259. Springer, Berlin, Heidelberg.

  36. Ricciardi W, Cascini F (2021) Guidelines and safety practices for improving patient safety. In Textbook Patient Safe Clin Risk Manag 3–18. Springer, Cham.

  37. Şahin M (2021) A comprehensive analysis of weighting and multicriteria methods in the context of sustainable energy. Int J Environ Sci Technol 18(6):1591–1616

    Article  CAS  Google Scholar 

  38. Eghbali-Zarch M, Tavakkoli-Moghaddam R, Dehghan-Sanej K, Kaboli A (2021) Prioritizing the effective strategies for construction and demolition waste management using fuzzy IDOCRIW and WASPAS methods. Eng Construction Architectural Manag

  39. Baykasoğlu A, Gölcük İ (2019) Revisiting ranking accuracy within WASPAS method. Kybernetes.

  40. Stojić G, Stević Ž, Antuchevičienė J, Pamučar D, Vasiljević M (2018) A novel rough WASPAS approach for supplier selection in a company manufacturing PVC carpentry products. Information 9(5):121

    Article  Google Scholar 

  41. Jahan A, Edwards KL (2013) Weighting of dependent and target-based criteria for optimal decision-making in materials selection process: biomedical applications. Mater Des 49:1000–1008

    Article  CAS  Google Scholar 

  42. Zadeh LA (1996) Fuzzy sets. InFuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh 394–432

  43. Shan M, Chan AP, Le Y, Xia B, Hu Y (2015) Measuring corruption in public construction projects in China. J Professional Issues Eng Educ Practice 141(4):05015001

    Article  Google Scholar 

  44. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci 8(3):199–249

    Article  Google Scholar 

  45. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning—II. Inf Sci 8(4):301–357

    Article  Google Scholar 

  46. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-III. Inf Sci 9(1):43–80

    Article  Google Scholar 

  47. Zhang X, Ma W, Chen L (2014) New similarity of triangular fuzzy number and its application. Sci World Journal 1:2014

    Google Scholar 

  48. Baležentis A, Baležentis T, Brauers WK (2012) Personnel selection based on computing with words and fuzzy MULTIMOORA. Expert Syst Appl 39(9):7961–7967

    Article  Google Scholar 

  49. Zardari NH, Ahmed K, Shirazi SM, Yusop ZB (2015) Weighting methods and their effects on multi-criteria decision making model outcomes in water resources management. Springer

  50. Wang JJ, Jing YY, Zhang CF, Zhao JH (2009) Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renew Sustain Energy Rev 13(9):2263–2278

    Article  Google Scholar 

  51. Zoraghi N, Amiri M, Talebi G, Zowghi M (2013) A fuzzy MCDM model with objective and subjective weights for evaluating service quality in hotel industries. J Industrial EngInt 9(1):1–3

    Google Scholar 

  52. Liu H, Kong F (2005) A new MADM algorithm based on fuzzy subjective and objective integrated weights. Int J Inform Syst Sci Comput Inform 1(3–4):420–427

    Google Scholar 

  53. Lotfi FH, Fallahnejad R (2010) Imprecise ‘Shannon’s entropy and multi attribute decision making. Entropy 12(1):53–62

    Article  Google Scholar 

  54. Zavadskas EK, Turskis Z, Antucheviciene J, Zakarevicius A (2012) Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika 122(6):3–6

    Article  Google Scholar 

  55. Turskis Z, Zavadskas EK, Antucheviciene J, Kosareva N (2015) A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. Int J Comput Communications Control 10(6):113–128

    Article  Google Scholar 

  56. Zavadskas EK, Antucheviciene J, Hajiagha SH, Hashemi SS (2014) Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (WASPAS-IVIF). Appl Soft Comput 1(24):1013–1021

    Article  Google Scholar 

  57. Turskis Z, Goranin N, Nurusheva A, Boranbayev S (2019) A fuzzy WASPAS-based approach to determine critical information infrastructures of EU sustainable development. Sustainability 11(2):424

    Article  Google Scholar 

  58. Hafezalkotob A, Hami-Dindar A, Rabie N, Hafezalkotob A (2018) A decision support system for agricultural machines and equipment selection: a case study on olive harvester machines. Comput Electron Agric 1(148):207–216

    Article  Google Scholar 

  59. Hafezalkotob A, Hafezalkotob A (2017) Interval target-based VIKOR method supported on interval distance and preference degree for machine selection. Eng Appl Artif Intell 1(57):184–196

    Article  Google Scholar 

  60. American Diabetes Association (2020) Introduction: standards of medical care in diabetes. Diabetes Care 43(Supplement 1):S1–S2

    Google Scholar 

  61. Care D (2019) Standards of medical care in diabetes 2019. Diabetes Care 42(Suppl 1):S124-138

    Google Scholar 

  62. American Diabetes Association (2003) Standards of medical care for patients with diabetes mellitus. Diabetes Care 26(suppl 1):s33-50

    Article  Google Scholar 

  63. Type 2 diabetes, American diabetes association; [cited 2021 Nov 22]. Available from: http:// https://www.diabetes.org/diabetes/type-2

  64. Type 2 diabetes, Mayo clinic; [cited 2021 Nov 22]. Available from: https://www.mayoclinic.org/diseases-conditions/type-2-diabetes/diagnosis-treatment/drc-20351199

  65. Insulin, Medicines, & Other Diabetes Treatments, U.S. Department of Health and Human Services, National Institutes of Health; [cited 2021 Nov 22]. Available from: https://www.niddk.nih.gov/health-information/diabetes/overview/insulin-medicines-treatments

  66. Moin T, Martin JM, Mangione CM, Grotts J, Turk N, Norris KC, Tseng CH, Jeffers KS, Castellon-Lopez Y, Frosch DL, Duru OK (2021) Choice of intensive lifestyle change and/or metformin after shared decision making for diabetes prevention: results from the Prediabetes Informed Decisions and Education (PRIDE) Study. Med Decision Making, p.0272989X211001279

  67. IDF Clinical Practice Recommendations for Managing Type 2 Diabetes in Primary Care, 2018 [cited 2021 Nov 22]. Available from: https://www.idf.org/e-library/guidelines/128-idf-clinical-practice-recommendations-for-managing-type-2-diabetes-in-primary-care.html

  68. American Diabetes Association (2021) 9. Pharmacologic approaches to glycemic treatment: standards of Medical Care in Diabetes—2021. Diabetes Care 44(Supplement 1):S111–24.

  69. Zhang Y, McCoy RG, Mason JE, Smith SA, Shah ND, Denton BT (2014) Second-line agents for glycemic control for type 2 diabetes: are newer agents better? Diabetes Care 37(5):1338–1345

    Article  CAS  PubMed  Google Scholar 

  70. Eghbali-Zarch M, Tavakkoli-Moghaddam R, Esfahanian F, Azaron A, Sepehri MM (2019) A Markov decision process for modeling adverse drug reactions in medication treatment of type 2 diabetes. Proc Inst Mech Eng [H] 233(8):793–811

    Article  Google Scholar 

  71. Bramante CT, Lee CJ, Gudzune KA (2017) Treatment of obesity in patients with diabetes. Diabetes Spectrum 30(4):237–243

    Article  PubMed  PubMed Central  Google Scholar 

  72. Razzaghi T, Safro I, Ewing J, Sadrfaridpour E, Scott JD (2019) Predictive models for bariatric surgery risks with imbalanced medical datasets. Ann Oper Res 280(1):1–8

    Article  Google Scholar 

  73. Bonnet F, Scheen A (2017) Understanding and overcoming metformin gastrointestinal intolerance. Diabetes Obes Metab 19(4):473–481

    Article  CAS  PubMed  Google Scholar 

  74. Einarson TR, Acs A, Ludwig C, Panton UH (2018) Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007–2017. Cardiovasc Diabetol 17(1):1–9

    Article  Google Scholar 

  75. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26

    Article  Google Scholar 

  76. Athawale VM, Chakraborty S (2011) A comparative study on the ranking performance of some multi-criteria decision-making methods for industrial robot selection. Int J Ind Eng Comput 2(4):831–850

    Google Scholar 

  77. Khunti K, Gomes MB, Pocock S, Shestakova MV, Pintat S, Fenici P, Hammar N, Medina J (2018) Therapeutic inertia in the treatment of hyperglycaemia in patients with type 2 diabetes: a systematic review. Diabetes Obes Metab 20(2):427–437

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran

    Maryam Eghbali-Zarch

  2. School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Reza Tavakkoli-Moghaddam

  3. Department of Endocrinology and Metabolism, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Fatemeh Esfahanian

  4. Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI, 48202, USA

    Sara Masoud

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  1. Maryam Eghbali-Zarch
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  2. Reza Tavakkoli-Moghaddam
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  3. Fatemeh Esfahanian
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Correspondence to Maryam Eghbali-Zarch.

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Eghbali-Zarch, M., Tavakkoli-Moghaddam, R., Esfahanian, F. et al. Prioritizing the glucose-lowering medicines for type 2 diabetes by an extended fuzzy decision-making approach with target-based attributes. Med Biol Eng Comput 60, 2423–2444 (2022). https://doi.org/10.1007/s11517-022-02602-3

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