• This record comes from PubMed

Multicriteria Decision-Making in Diabetes Management and Decision Support: Systematic Review

. 2024 Feb 01 ; 12 () : e47701. [epub] 20240201

Status PubMed-not-MEDLINE Language English Country Canada Media electronic

Document type Journal Article, Review

Links

PubMed 38300703
PubMed Central PMC10870205
DOI 10.2196/47701
PII: v12i1e47701
Knihovny.cz E-resources

BACKGROUND: Diabetes mellitus prevalence is increasing among adults and children around the world. Diabetes care is complex; examining the diet, type of medication, diabetes recognition, and willingness to use self-management tools are just a few of the challenges faced by diabetes clinicians who should make decisions about them. Making the appropriate decisions will reduce the cost of treatment, decrease the mortality rate of diabetes, and improve the life quality of patients with diabetes. Effective decision-making is within the realm of multicriteria decision-making (MCDM) techniques. OBJECTIVE: The central objective of this study is to evaluate the effectiveness and applicability of MCDM methods and then introduce a novel categorization framework for their use in this field. METHODS: The literature search was focused on publications from 2003 to 2023. Finally, by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, 63 articles were selected and examined. RESULTS: The findings reveal that the use of MCDM methods in diabetes research can be categorized into 6 distinct groups: the selection of diabetes medications (19 publications), diabetes diagnosis (12 publications), meal recommendations (8 publications), diabetes management (14 publications), diabetes complication (7 publications), and estimation of diabetes prevalence (3 publications). CONCLUSIONS: Our review showed a significant portion of the MCDM literature on diabetes. The research highlights the benefits of using MCDM techniques, which are practical and effective for a variety of diabetes challenges.

See more in PubMed

Forouhi NG, Wareham NJ. Epidemiology of diabetes. Medicine. 2010;38(11):602–606. doi: 10.1016/j.mpmed.2010.08.007. PubMed DOI PMC

IDF diabetes atlas 2021—10th edition. International Diabetes Federation. [2023-12-29]. https://diabetesatlas.org/atlas/tenth-edition/ PubMed

Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15:104–116. doi: 10.1016/j.csbj.2016.12.005. S2001-0370(16)30073-3 PubMed DOI PMC

Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103(2):137–149. doi: 10.1016/j.diabres.2013.11.002. S0168-8227(13)00385-9 PubMed DOI

Ogurtsova K, da Rocha Fernandes JD, Huang Y, Linnenkamp U, Guariguata L, Cho NH, Cavan D, Shaw JE, Makaroff LE. IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017;128:40–50. doi: 10.1016/j.diabres.2017.03.024.S0168-8227(17)30375-3 PubMed DOI

Zulqarnain M, Dayan F, Saeed M. TOPSIS analysis for the prediction of diabetes based on general characteristics of humans. Int J Pharm Sci Res. 2018;9(7):2932–2939. doi: 10.13040/IJPSR.0975-8232.9(7).2932-2939. DOI

Abdulkareem SA, Radhi HY, Fadil YA, Mahdi H. Soft computing techniques for early diabetes prediction. Indones J Electr Eng Comput Sci. 2022;25(2):1167–1176. doi: 10.11591/ijeecs.v25.i2.pp1167-1176. DOI

Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res. 2018;20(5):e10775. doi: 10.2196/10775. v20i5e10775 PubMed DOI PMC

Grant RW, Wexler DJ, Watson AJ, Lester WT, Cagliero E, Campbell EG, Nathan DM. How doctors choose medications to treat type 2 diabetes: a national survey of specialists and academic generalists. Diabetes Care. 2007;30(6):1448–1453. doi: 10.2337/dc06-2499. dc06-2499 PubMed DOI PMC

American Diabetes Association Diagnosis and classification of diabetes mellitus. Diabetes Care. 2014;37(Suppl 1):S81–S90. doi: 10.2337/dc14-S081. 37/Supplement_1/S81 PubMed DOI

Montori VM. Selecting the right drug treatment for adults with type 2 diabetes. BMJ. 2016;352:i1663. doi: 10.1136/bmj.i1663. PubMed DOI

Diabetes medication choice decision conversation aid. Welcome to the Diabetes Medication Choice Decision Conversation Aid. [2023-09-07]. https://diabetesdecisionaid.mayoclinic.org/index .

Dolan JG. Multi-criteria clinical decision support: a primer on the use of multiple criteria decision making methods to promote evidence-based, patient-centered healthcare. Patient. 2010;3(4):229–248. doi: 10.2165/11539470-000000000-00000. PubMed DOI PMC

Maruthur NM, Joy SM, Dolan JG, Shihab HM, Singh S. Use of the analytic hierarchy process for medication decision-making in type 2 diabetes. PLoS One. 2015;10(5):e0126625. doi: 10.1371/journal.pone.0126625. PONE-D-14-36463 PubMed DOI PMC

Peteiro-Barral D, Remeseiro B, Méndez R, Penedo MG. Evaluation of an automatic dry eye test using MCDM methods and rank correlation. Med Biol Eng Comput. 2017;55(4):527–536. doi: 10.1007/s11517-016-1534-5.10.1007/s11517-016-1534-5 PubMed DOI

Adhikary P, Kundu S. MCDA or MCDM based selection of transmission line conductor: small hydropower project planning and development. Int J Eng Res Appl. 2014;4(2):357–361.

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–269, W64. doi: 10.7326/0003-4819-151-4-200908180-00135. 0000605-200908180-00135 PubMed DOI

Borissova D. An overview of multi-criteria decision making models and software systems. In: Atanassov KT, editor. Research in Computer Science in the Bulgarian Academy of Sciences. Cham, Switzerland: Springer International Publishing; 2021. pp. 305–323.

Aruldoss M, Lakshmi TM, Venkatesan VP. A survey on multi criteria decision making methods and its applications. Am J Inf Syst. 2013;1(1):31–43. doi: 10.12691/ajis-1-1-5. DOI

Singh A, Malik SK. Major MCDM techniques and their application-a review. IOSR J Eng. 2014;4(5):15–25. doi: 10.9790/3021-04521525. DOI

Azhar NA, Radzi NAM, Ahmad WSHMW. Multi-criteria decision making: a systematic review. Recent Adv Electr Electron Eng. 2021;14(8):779–801. doi: 10.2174/2352096514666211029112443. DOI

Kangas J, Kangas A, Leskinen P, Pykäläinen J. MCDM methods in strategic planning of forestry on state‐owned lands in Finland: applications and experiences. Multi Criteria Decision Anal. 2002;10(5):257–271. doi: 10.1002/mcda.306. DOI

Saaty TL. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology. 1977 Jun;15(3):234–281. doi: 10.1016/0022-2496(77)90033-5. DOI

Hwang CL, Yoon K. Multiple Attribute Decision Making: Methods and Applications: A State-of-the-art Survey. Berlin Heidelberg: Springer; 1981. Methods for multiple attribute decision making; pp. 58–191.

Saaty TL. Decision Making with Dependence and Feedback: The Analytic Network Process. Pittsburgh: RWS publications; 1996.

Pamučar D, Stević Ž, Sremac S. A new model for determining weight coefficients of criteria in MCDM models: Full Consistency Method (FUCOM) Symmetry. 2018;10(9):393. doi: 10.3390/sym10090393. DOI

Triantaphyllou E. Multi-Criteria Decision Making Methods: A Comparative Study. Boston, MA: Springer US; 2000. Multi-criteria decision making methods; pp. 5–21.

Eghbali-Zarch M, Tavakkoli-Moghaddam R, Esfahanian F, Masoud S. Prioritizing the glucose-lowering medicines for type 2 diabetes by an extended fuzzy decision-making approach with target-based attributes. Med Biol Eng Comput. 2022;60(8):2423–2444. doi: 10.1007/s11517-022-02602-3.10.1007/s11517-022-02602-3 PubMed DOI

Eghbali-Zarch M, Tavakkoli-Moghaddam R, Esfahanian F, Sepehri MM, Azaron A. Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment. Artif Intell Med. 2018;87:20–33. doi: 10.1016/j.artmed.2018.03.003.S0933-3657(17)30522-5 PubMed DOI

Zhang Y, McCoy RG, Mason JE, Smith SA, Shah ND, Denton BT. Second-line agents for glycemic control for type 2 diabetes: are newer agents better? Diabetes Care. 2014;37(5):1338–1345. doi: 10.2337/dc13-1901. dc13-1901 PubMed DOI

Maruthur NM, Joy S, Dolan J, Segal JB, Shihab HM, Singh S. Systematic assessment of benefits and risks: study protocol for a multi-criteria decision analysis using the analytic hierarchy process for comparative effectiveness research. F1000Res. 2013;2:160. doi: 10.12688/f1000research.2-160.v1. PubMed DOI PMC

Nag K, Helal M. Multicriteria inventory classification of diabetes drugs using a comparison of AHP and fuzzy AHP models. 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM); December 16-19, 2018; Bangkok, Thailand. IEEE; 2018. pp. 1456–1460. DOI

Chen RC, Chiu JY, Batj CT. The recommendation of medicines based on multiple criteria decision making and domain ontology—an example of anti-diabetic medicines. 2011 International Conference on Machine Learning and Cybernetics; July 10-13, 2011; Guilin, China. IEEE; 2011. pp. 27–32. DOI

Wang M, Liu YW, Li X. Type-2 diabetes management using analytic hierarchy process and analytic network process. Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control; April 07-09, 2014; Miami, FL, USA. IEEE; 2014. pp. 655–660. DOI

Bao Y, Gao B, Meng M, Ge B, Yang Y, Ding C, Shi B, Tian L. Impact on decision making framework for medicine purchasing in Chinese public hospital decision-making: determining the value of five Dipeptidyl Peptidase 4 (DPP-4) inhibitors. BMC Health Serv Res. 2021;21(1):807. doi: 10.1186/s12913-021-06827-0. 10.1186/s12913-021-06827-0 PubMed DOI PMC

Onar SC, Ibil EH. A decision support system proposition for type-2 diabetes mellitus treatment using spherical fuzzy AHP method. In: Tolga AC, Oztaysi B, Kahraman C, Sari IU, Cebi S, Onar SC, editors. Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference, Held August 24-26, 2021. Volume 2. Cham, Switzerland: Springer International Publishing; 2021. pp. 749–756.

Zhang LD, Cui X, Liu FM, Xie YM, Zhang Q. Clinical comprehensive evaluation of Mudan Granules in treatment of diabetic peripheral neuropathy with qi-deficiency and collateral stagnation syndrome. Zhongguo Zhong Yao Za Zhi. 2021;46(23):6078–6086. doi: 10.19540/j.cnki.cjcmm.20210930.501. PubMed DOI

Cai T, Wu H, Qin J, Qiao J, Yang Y, Wu Y, Qiao D, Xu H, Cao Y. In vitro evaluation by PCA and AHP of potential antidiabetic properties of lactic acid bacteria isolated from traditional fermented food. LWT. 2019;115:108455. doi: 10.1016/j.lwt.2019.108455. DOI

Sekar KR, Yogapriya S, Anand NS, Venkataraman V. Ranking diabetic mellitus using improved PROMETHEE hesitant fuzzy for healthcare systems. In: Chen JIZ, Hemanth J, Bestak R, editors. Intelligent Data Communication Technologies and Internet of Things: Proceedings of ICICI 2020. Singapore: Springer Nature; 2021. pp. 709–724.

Mühlbacher AC, Bethge S, Kaczynski A, Juhnke C. Patients preferences regarding the treatment of type II diabetes mellitus: comparison of best-worst scaling and analytic hierarchy process. Value Health. 2013;16(7):A446. doi: 10.1016/j.jval.2013.08.707. DOI

Mahat N, Ahmad S. Selection of the best thermal massage treatment for diabetes by using fuzzy analytical hierarchy process. J Comput Res Innov. 2018;2(1):23–28. doi: 10.24191/jcrinn.v2i1.25. DOI

Pan D, Wang K, Zhou Z, Liu X, Shen J. FAHP-based mathematical model for exercise rehabilitation management of diabetes mellitus. ArXiv. Preprint posted online on January 7 2022. doi: 10.48550/arXiv.2201.07884. DOI

Rani P, Mishra AR, Mardani A. 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. 2020;94:106441. doi: 10.1016/j.asoc.2020.106441. DOI

Balubaid MA, Basheikh MA. Using the analytic hierarchy process to prioritize alternative medicine: selecting the most suitable medicine for patients with diabetes. Int J Basic Appl Sci. 2016;5(1):67. doi: 10.14419/ijbas.v5i1.5607. DOI

Mühlbacher AC, Bethge S, Kaczynski A, Juhnke C. 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. 2016;78(5):326–336. doi: 10.1055/s-0034-1390474. PubMed DOI

Abbasi M, Khorasani ZM, Etminani K, Rahmanvand R. Determination of the most important risk factors of gestational diabetes in Iran by group analytical hierarchy process. Int J Reprod Biomed. 2017;15(2):109–114. PubMed PMC

Yas QM, Adday BN, Abed AS. Evaluation multi diabetes mellitus symptoms by integrated fuzzy-based MCDM approach. Turk J Comput Math Educ. 2021;12(13):4069–4082.

Amin-Naseri MR, Neshat N. An expert system based on analytical hierarchy process for Diabetes Risk Assessment (DIABRA) In: Wang G, Chai Y, Tan Y, Shi Y, editors. Advances in Swarm Intelligence, Part II: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part II. Berlin Heidelberg: Springer; 2011. pp. 252–259.

El-Sappagh S, Alonso JM, Ali F, Ali A, Jang J, Kwak K. An ontology-based interpretable fuzzy decision support system for diabetes diagnosis. IEEE Access. 2018;6:37371–37394. doi: 10.1109/access.2018.2852004. DOI

Baha BY, Wajiga GM, Blamah NV, Adewumi AO. Analytical hierarchy process model for severity of risk factors associated with type 2 diabetes. Sci Res Essays. 2013;8(39):1906–1910.

Sharma S, Sharma B. EDAS based selection of machine learning algorithm for diabetes detection. 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART); December 04-05, 2020; Moradabad, India. IEEE; 2020. pp. 240–244.

Malapane J, Doorsamy W, Paul BS. Prediction analysis using weighted product method to compare machine learning algorithms for diabetes disease. Int J Res Eng. 2022 Sep 04;5(9):49–53.

Felix A, Kumar RS, Parthiban A. Soft computing decision making system to analyze the risk factors of T2DM. AIP Conf Proc. 2019;2112:020086-1–020086-12. doi: 10.1063/1.5112271. DOI

Sankar A, Jeyaraj GT. Extreme learning machine and K-means clustering for the improvement of link prediction in social networks using analytic hierarchy process. Int J Enterp Netw Manag. 2019;10(3/4):371–388. doi: 10.1504/ijenm.2019.10024740. DOI

Bondor CI, Mureşan A. Correlated criteria in decision models: recurrent application of TOPSIS method. Appl Med Inform. 2012;30(1):55–63.

Gaikwad SM, Mulay P, Joshi RR. Analytical hierarchy process to recommend an ice cream to a diabetic patient based on sugar content in it. Procedia Comput Sci. 2015;50:64–72. doi: 10.1016/j.procs.2015.04.062. DOI

Sharawat K, Dubey SK. Diet recommendation for diabetic patients using MCDM approach. In: Gehlot A, Singh R, Choudhury S, editors. Intelligent Communication, Control and Devices: Proceedings of ICICCD 2017. Singapore: Springer Nature; 2018. pp. 239–246.

Santoso I, Sa’adah M, Wijana S. QFD and fuzzy AHP for formulating product concept of probiotic beverages for diabetic. TELKOMNIKA. 2017;15(1):391–398. doi: 10.12928/telkomnika.v15i1.3555. DOI

Zadeh MSAT, Li J, Alian S. Personalized meal planning for diabetic patients using a multi-criteria decision- making approach. 2019 IEEE International Conference on E-health Networking, Application & Services (HealthCom); October 14-16, 2019; Bogota, Colombia. IEEE; 2019. pp. 1–6. DOI

Gulint G, Kadam K. Recommending food replacement shakes along with ice cream for diabetic patients using AHP and TOPSIS to control blood glucose level. Int J Eng Trends Technol. 2016;34(5):243–251. doi: 10.14445/22315381/ijett-v34p250. DOI

Gaikwad SM, Joshi RR, Mulay P. Analytical Network Process (ANP) to recommend an ice cream to a diabetic patient. Int J Comput Appl. 2015;121(12):49–52. doi: 10.5120/21596-4692. DOI

Gaikwad SM, Joshi RR, Kulkarni AJ. Swarm, Evolutionary, and Memetic Computing: 6th International Conference, SEMCCO 2015, Hyderabad, India, December 18-19, 2015, Revised Selected Papers. Cham: Springer International Publishing; 2016. Cohort intelligence and genetic algorithm along with AHP to recommend an ice cream to a diabetic patient; pp. 40–49.

Gaikwad SM, Joshi R, Gaikwad SM. Modified analytical hierarchy process to recommend an ice cream to a diabetic patient. ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies; March 4-5, 2016; Udaipur, India. 2016. pp. 1–5. DOI

Gupta K, Roy S, Poonia RC, Nayak SR, Kumar R, Alzahrani KJ, Alnfiai MM, Al-Wesabi FN. Evaluating the usability of mHealth applications on type 2 diabetes mellitus using various MCDM methods. Healthcare (Basel) 2021;10(1):4. doi: 10.3390/healthcare10010004. healthcare10010004 PubMed DOI PMC

Wang X, He L, Zhu K, Zhang S, Xin L, Xu W, Guan Y. An integrated model to evaluate the impact of social support on improving self-management of type 2 diabetes mellitus. BMC Med Inform Decis Mak. 2019;19(1):197. doi: 10.1186/s12911-019-0914-9. 10.1186/s12911-019-0914-9 PubMed DOI PMC

Mishra V, Samuel C, Sharma SK. Supply chain partnership assessment of a diabetes clinic. Int J Health Care Qual Assur. 2018;31(6):646–658. doi: 10.1108/IJHCQA-06-2017-0113. PubMed DOI

Mishra V. Customized quality assessment framework for diabetes care. Int J Qual Res. 2020;14(1):129–146. doi: 10.24874/ijqr14.01-09. DOI

Mishra V. Planning and selection of facility layout in healthcare services. Hosp Top. 2022:1–9. doi: 10.1080/00185868.2022.2088433. PubMed DOI

Byun DH, Chang RS, Park MB, Son HR, Kim CB. Prioritizing community-based intervention programs for improving treatment compliance of patients with chronic diseases: applying an analytic hierarchy process. Int J Environ Res Public Health. 2021;18(2):455. doi: 10.3390/ijerph18020455. ijerph18020455 PubMed DOI PMC

Mehrotra S, Kim K. Outcome based state budget allocation for diabetes prevention programs using multi-criteria optimization with robust weights. Health Care Manag Sci. 2011;14(4):324–337. doi: 10.1007/s10729-011-9166-7. PubMed DOI

Haji M, Kerbache L, Al-Ansari T. Evaluating the performance of a safe insulin supply chain using the AHP-TOPSIS approach. Processes. 2022;10(11):2203. doi: 10.3390/pr10112203. DOI

Suka M, Ichimura T, Yoshida K. Clinical decision support system applied the analytic hierarchy process. In: Palade V, Howlett RJ, Jain L, editors. Knowledge-Based Intelligent Information and Engineering Systems, LNCS 2774. Berlin Heidelberg: Springer; 2003. pp. 417–423.

Fico G, Cancela J, Arredondo MT, Dagliati A, Sacchi L, Segagni D, Millana AM, Fernandez-Llatas C, Traver V, Sambo F, Facchinetti A, Verdu J, Guillén A, Bellazzi R, Cobelli C. User requirements for incorporating diabetes modeling techniques in disease management tools. In: Lackovic I, Vasic D, editors. 6th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proceedings, vol 45. Cham: Springer; 2015. pp. 992–995.

Long MD, Centor R. 236 utilizing pairwise comparisons to determine relative importance of diabetes guidelines: a comparison of physician and patient perspectives. J Investig Med. 2005;53(1):S294. doi: 10.2310/6650.2005.00006.235. DOI

Gajdoš O, Juřičková I, Otawova R. Health technology assessment models utilized in the chronic care management. In: Ortuño F, Rojas I, editors. Bioinformatics and Biomedical Engineering. IWBBIO 2015. Lecture Notes in Computer Science, vol 9043. Cham: Springer; 2015. pp. 54–65.

Gupta K, Roy S, Poonia RC, Kumar R, Nayak SR, Altameem A, Saudagar AKJ. Multi-criteria usability evaluation of mHealth applications on type 2 diabetes mellitus using two hybrid MCDM models: CODAS-FAHP and MOORA-FAHP. Appl Sci. 2022;12(9):4156. doi: 10.3390/app12094156. DOI

Chang HY, Lo CL, Chang HL. Development of the benefit-risk assessment of complementary and alternative medicine use in people with diabetes: a Delphi-analytic hierarchy process approach. Comput Inform Nurs. 2021;39(7):384–391. doi: 10.1097/CIN.0000000000000749. 00024665-202107000-00008 PubMed DOI

Ebrahimi M, Ahmadi K. Diabetes-related complications severity analysis based on hybrid fuzzy multi-criteria decision making approaches. Iran J Med Inform. 2017;6(1):11. doi: 10.24200/ijmi.v6i1.129. DOI

Ahmadi K, Ebrahimi M. A novel algorithm based on information diffusion and fuzzy MADM methods for analysis of damages caused by diabetes crisis. Appl Soft Comput. 2019;76:205–220. doi: 10.1016/j.asoc.2018.12.004. DOI

Bondor CI, Kacso IM, Lenghel AR, Mureşan A. Hierarchy of risk factors for chronic kidney disease in patients with type 2 diabetes mellitus. 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing; August 30-September 01, 2012; Cluj-Napoca, Romania. IEEE; 2012. pp. 103–106. DOI

Ahmed S, Roy S, Alam GR. Benchmarking and selecting optimal diabetic retinopathy detecting machine learning model using entropy and TOPSIS method. 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME); October 07-08, 2021; Mauritius, Mauritius. IEEE; 2021. pp. 1–6. DOI

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

Alassery F, Alzahrani A, Khan AI, Khan A, Nadeem M, Ansari MTJ. Quantitative evaluation of mental-health in type-2 diabetes patients through computational model. Intell Autom Soft Comput. 2022;32(3):1701–1715. doi: 10.32604/iasc.2022.023314. DOI

Wang CC, Yang CH, Wang CS, Xu D, Huang BS. Artificial neural networks in the selection of shoe lasts for people with mild diabetes. Med Eng Phys. 2019;64:37–45. doi: 10.1016/j.medengphy.2018.12.014.S1350-4533(19)30001-3 PubMed DOI

Jain R, Kathuria A, Mukhopadhyay D, Gupta M. Fuzzy MCDM: application in disease risk and prediction. In: Devi KG, Rath M, Linh NTD, editors. Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches. Boca Raton, FL: CRC Press; 2020. pp. 55–70.

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...