JMIR MEDICAL INFORMATICS

Aldaghi & Muzik

ReviewMulticriteria Decision-Making in Diabetes Management and
Decision Support: Systematic Review
Tahmineh Aldaghi1*, MSc; Jan Muzik2*, PhD
1Spin-off Companies and Research Results Commercialization Center, First Faculty of Medicine, Charles University, Prague, Czech Republic2

Department of Information and Communication Technologies in Medicine, Faculty of Biomedical Engineering, Czech Technical University, Prague,
Czech Republic*

all authors contributed equally

Corresponding Author:
Jan Muzik, PhD
Department of Information and Communication Technologies in Medicine
Faculty of Biomedical Engineering
Czech Technical University
Studničkova 7
Prague, 128 00
Czech Republic
Phone: 420 777568945
Email: jan.muzik@cvut.cz

Abstract
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 thechallenges faced by diabetes clinicians who should make decisions about them. Making the appropriate decisions will reduce thecost 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 thenintroduce 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: theselection 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 (3publications).
Conclusions: Our review showed a significant portion of the MCDM literature on diabetes. The research highlights the benefitsof using MCDM techniques, which are practical and effective for a variety of diabetes challenges.
(JMIR Med Inform 2024;12:e47701) doi: 10.2196/47701
KEYWORDS
analytical hierarchy process; diabetes management; diabetes recognition; glucose management; multi-criteria decision making;technique for order of preference by similarity to ideal solution; decision support; diabetes; diabetic; glucose; blood sugar; review
methodology; systematic review; decision making; self-management; digital health tool

Introduction
Overview
Diabetes mellitus is a chronic disease that is characterized by
impaired insulin production and action [1]. According to the
etiopathology of diabetes, the 3 most common clinical categories
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are distinguished: type 1 diabetes, type 2 diabetes (T2D), andgestational diabetes mellitus [2,3]. In recent decades, diabetes
prevalence has increased in both adults and children around theworld. By 2035, there will be an estimated 592 million people
worldwide with diabetes [4]. By 2040, this number is expected
to rise to 642 million [5], and by 2045, there will be 783.2
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million cases of diabetes worldwide [2]. According to the global
2021 findings of the International Diabetes Federation (IDF),
537 million adults are living with diabetes, and 3 in 4 of them
reside in low- and middle-income countries. In 2021, a total of
6.7 million people died of diabetes, equating to 1 death every
5 seconds. The expenditure on diabetes-related health care is atleast US $966 billion, and it has increased up to 316% over thelast 15 years [2].
Diabetes is a chronic condition requiring continuous medicalcare and patient education to prevent severe complications andlong-term risks. Managing diabetes involves addressing various
aspects of the patient’s health, including blood glucose
monitoring, monitoring and managing carbohydrate intake,regular engagement in physical activity, and medication
management. By understanding the disease’s nuances andrecognizing when it might become severe, people can take steps
to protect their well-being. Thus, faster diagnosis of diabetes
and its potential complications is crucial for both patients andhealth care providers [6]. General practitioners faced asignificant problem when diagnosing diabetes, partly because
patients displayed a wide range of signs and symptoms. This
complex clinical environment confused general practitioners
and changed the diagnostic procedure into a multiobjective
health care decision-making challenge [7].
In addition to making informed decisions about the patient's
health, endocrinologists and general practitioners shouldcarefully assess various factors, including lifestyle choices,dietary habits, daily physical activity levels, insulin
requirements, and the patient’s willingness to embrace
self-management technologies such as insulin pumps or pens,
smart bracelets, continuous glucose monitoring, and mobile
apps [8]. This comprehensive evaluation enables them to selectthe most appropriate treatment options. As an illustration, whenit comes to managing hyperglycemia in patients with T2D, there
is a diverse array of treatment options available. Currently,
approximately 30 medications belonging to 9 distinct therapeutic
categories have received approval for use, with ongoing researchhttps://medinform.jmir.org/2024/1/e47701

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Aldaghi & Muzik
and development efforts yielding additional drugs and novel
drug categories [9]. Due to the variety of options and guidelines
from organizations such as the American Diabetes Association(ADA) [10], doctors often customize prescriptions using
different doses and combinations for effective diabetes
management [9]. The available medications vary in efficacy,
safety, dosage, side effects, and cost. A lack of comparative
information across these factors often leaves patients andphysicians unable to make well-informed decisions [11]. The
selection of diabetes medication presents itself as amultiobjective problem within the realm of health caredecision-making [9].
Medical decision support could play a pivotal role in enhancing
health care decision-making as it integrates pertinent, organizedclinical knowledge and patient data into health-related decisions
and processes [12]. Multiple stakeholders, including patients,
health care providers, and those involved in patient care, canreceive a mix of general clinical insights, patient-specific data,or both. Therefore, a quantitative approach that combines
treatment benefits and drawbacks with individual preferencesto effectively guide medical decisions could be multicriteria
decision-making (MCDM) [13]. MCDM or multicriteria
decision analysis (MCDA) is a valuable subdiscipline ofoperations research, particularly beneficial when dealing withmultiple objectives, such as treatment-related outcomes, in
benefit-risk analysis [14,15]. A typical MCDM problem consistsof 4 key phases: option formulation, criteria selection, criteria
weighting, and the decision-making process [16].

Objective
By considering the abovementioned factors, the primary aimof this research is to assess the use and practicality of MCDM
methods in the context of diabetes. Our goal is to examine thevarious ways in which MCDM techniques have been used to
study diabetes and present an innovative categorization of theirapplications in this field. Figure 1 demonstrates the graphicalabstract of the paper.

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Figure 1. Graphical abstract of the paper. MCDM: multicriteria decision-making.

Methods
Search Strategies
A query was carried out on PubMed, Elsevier, Embase,
MEDLINE, Scopus, MBC, Springer, IEEE, MDPI, Taylor andFrancis Online, and Google Scholar based on published articles.
The keywords for our paper were extracted from Medical
Subject Headings (MeSH). The keywords “diabetes” and“glucose” were combined with MCDM techniques terms suchas TOPSIS, AHP, and multi-criteria-decision-making using theBoolean operator AND/OR. The specific query searched was:((diabetes OR glucose) AND (AHP OR TOPSIS OR MCDM
OR multi-criteria-decision-making)).

Inclusion and Exclusion Criteria
We initially eliminated any duplicate articles from various
sources after receiving the results of an initial collection of
relevant articles and then manually inspected the remaining
articles to assess them under the inclusion criteria. The inclusion
criteria were any English papers published between 2003 and2023. Research, review, conference, and case report articles
with an abstract or full text were taken into account.
Non-English articles and other research forms, such as lettersto editors and brief messages, were excluded. Out of almost
2210 articles, only 63 were found and chosen based on keywords
and all of our criteria. The article selection process was basedon PRISMA (Preferred Reporting Items for Systematic Review
and Meta-Analyses; Figure 2) [17].

Figure 2. PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) flowchart.

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Results
Overview
Based on Figure 2, after removing duplicates and examining
according to the inclusion and exclusion criteria, 63 publications
were included in the final evaluation. Based on our investigation
to reveal the frequency of publications in databases, it became
clear that most of the publications were indexed in Google
Scholar, with 60 publications; PubMed, with 17 publications;and Springer and IEEE, with 8 and 7 publications, respectively.
We initially provided a concise overview of MCDM and its
techniques, followed by the presentation of our research findings
gathered from reviewing publications.

MCDM Techniques Overview
Since so many choices in our modern lives depend on amultitude of factors, the decision can be made by giving various
criteria varying weights, which is done by expert groups.
Determining the structure and explicitly evaluating several
criteria is crucial. Therefore, constructing and resolving
multicriteria planning and decision-making challenges is referred
to as MCDM. As a result, MCDM is composed of a set ofnumerous criteria, a set of alternatives, and some sort ofcomparison between them [18-20].
No alternative optimizes all criteria uniformly in multicriteria
optimization assignments. Any solution to the multicriteria taskthat enhances a specific criterion can be examined, but the task
must ultimately have a preferred option. The decision maker

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must provide more details to select the best decision. Throughout
its brief history of about 50 years, MCDM has been aninteresting study topic [20]. There are 2 categories of MCDM
approaches: multiattribute decision-making (MADM) andmultiobjective decision-making (MODM) [19,20].
In order to find the optimal answer, decision makers in MADM
choose to categorize, rank, or prioritize a limited number ofchoices. Pairwise comparison, outranking, and distance-based
approaches are the 3 basic methods used in MADM. Pairwise
comparison involves evaluating and contrasting the weights ofseveral criteria using a base scale. Analytic hierarchy process(AHP) and analytical network process (ANP) are frequently
used in pairwise comparison [21]. Outranking approaches offer
a variety of options and determine whether one option has any
sort of dominance over the others [22]; instances of outranking
techniques include Elimination Et Choix Traduisant la Realité(ELECTRE) and preference ranking organization method forenrichment of evaluations (PROMETHEE) [21]. The solution
with the shortest distance to the ideal point is considered thebest according to distance-based techniques, which measure thedistance a solution is from the ideal point. The technique fororder of preference by similarity to ideal solution (TOPSIS)
and ViseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR) are 2 popular distance-based methodologies [21].
Unlike MADM, MODM handles situations where there aremany decision makers and an infinite number of possibilities.
All of these MCDM methods are presented in Figure 3. The
most efficient MCDM techniques are introduced in the following
sections.

Figure 3. Hierarchical structures of MCDM methods. AHP: analytic hierarchy process; ANP: analytical network process; ELECTRE: Elimination Et
Choix Traduisant la Realité; GA: genetic algorithm; GP: goal programming; MADM: multiattribute decision-making; MCDM: multicriteria
decision-making; MODM: multiobjective decision-making; PROMETHEE: preference ranking organization method for enrichment of evaluations;TOPSIS: technique for order of preference by similarity to ideal solution; VIKOR: ViseKriterijumska Optimizacija I Kompromisno Resenje.

AHP Method
Saaty [23] was the first to introduce the AHP. As shown in
Figure 4, AHP includes the decision’s objective at the top, thecriteria and subcriteria in the middle, and the collection of
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alternatives at the bottom [7]. The key benefits of AHP are its
scalability and ease of usage. AHP can be applied using Excel(Microsoft) or web-based tools such as Transparent Choice,
SpiceLogic, Decerns MCDA, MATLAB (MathWorks), R (RCore Team), and Super Decisions.
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Figure 4. Hierarchical structure of analytic hierarchy process.

TOPSIS Method
As shown in Figure 5, TOPSIS is a distance-based techniquethat Hwang and Yoon [24] proposed in 1981. The TOPSIS
technique makes it easy to define the positive and negative ideal
solutions by presuming that each criterion tends tomonotonically increase or reduce use. A Euclidean distanceapproach is suggested to assess how closely the alternatives
resemble the ideal solution. The preferred order of thealternatives will be determined by a series of comparisons of
their relative distances. The general principle behind thisapproach is that the optimal option should be closest to the ideal
solution and the farthest distance from the negative ideal
solution. In the ideal solution, the ideal solution has the best
attribute values, maximizes the benefit criteria, and minimizes
the cost criteria. In the negative ideal solution, the negative
solution has the worst attribute values, maximizes the costcriteria, and minimizes the benefit criteria [19,21].

Figure 5. TOPSIS method. TOPSIS: technique for order of preference by similarity to ideal solution.

ANP Method

do not use it often. Therefore, Saaty [25] suggested ANP as acontinuation of AHP. Decision makers are capable of making

Due to the inability of AHP to produce an adequate rating witha limited number of possibilities, the majority of organizations
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decisions in difficult situations, according to ANP's capability[21].

evident that, starting in 2016, the number of publications in thisarea has been steadily rising, reaching 10 in 2022.

Weighting Methods

Then, a new classification of the applications of MCDM
approaches in diabetes was proposed: (1) selection of diabetes
medication, (2) diagnosis of diabetes, (3) meal recommendation
for diabetes, (4) diabetes management, (5) diabetes
complication, and (6) estimation of diabetes prevalence.

One of the crucial phases of MCDM problems is determining
the weights of the criterion [26]. Several weighing techniques
can be divided into the following groups: (1) subjective
weighting method: AHP, Weighted Sum Model (WSM) [27],and Weighted Product Model (WPM) [27]; (2) objective
weighting method: Entropy method [28] and Criteria ImportanceThrough Intercriteria Correlation (CRITIC) [28]; and (3)integrated method: step-wise weigh assessment ratio analysis(SWARA) [29] and Weighted Aggregated Sum Product
Assessment (WASPAS) [28].

Selection of Diabetes Medication
Table 1 shows that approximately 30% (n=19/63) of the
publications focused on using MCDM techniques to determine
the optimal diabetes medication among various options. Notably,
AHP and fuzzy AHP, with 6 and 4 mentions, respectively, werethe most frequently used methods.

Following a thorough analysis of all of the MCDM publicationsin the field of diabetes research during a 2-decade period, it washttps://medinform.jmir.org/2024/1/e47701

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Table 1. Diabetes medication publications.
Reference

Methods

Objective

Results

Maruthur et al
[14]

AHPa

Select oral T2Db medications

Sitagliptin, sulfonylureas, and pioglitazone

Eghbali-Zarch etal [29]SWARAc method, ratio anal- Choose the pharmacological treatment
ysis, and the FMULTIMOO- for T2D
RAd method

Metformin should be used as the first-line medication,
followed by sulfonylurea, glucagon-like peptide-1 receptor agonist, dipeptidyl peptidase-4 inhibitor, and insulin

Eghbali-Zarch etal [28]WASPASe, entropy, andZhang et al [30]TOPSISg

Ranking of diabetes medicines

CDSSh can assist young doctors and nonspecialty
physicians with medication prescriptions

Maruthur et al
[31]

AHP

Select oral T2D medications

AHP will aid, support, and enhance the ability of decision makers to make evidence-based informed decisions
consistent with their values and preferences

Nag and Helal[32]

Fuzzy AHP and AHPClassification of diabetic medications

Fuzzy AHP model can better handle the ambiguity ofdecision makers

Chen et al [33]

Entropy

Choose pharmaceuticals

AGIi, DPP4j, METk, Glinide, SUl, and TZDm

Wang et al [34]

AHP and ANPn

Combine different clinical, economic,
and medical decision-making elements

Modifying one's lifestyle, taking metformin, and receiving insulin injections

Bao et al [35]MCDAo

Assess medicine for diabetes

Five DPP4 inhibitors was valuable

Onar and Ibil
[36]

Fuzzy AHP

Considered the best oral antidiabetic

Proposed a decision support system

Zhang et al [37]MCDA

Examine the Mudan Granules

The new medication was acceptable

Cai et al [38]

AHP

Evaluate strains of the efficacy of the

Potential antidiabetic effectf

CRITIC

Determine the final ranking of the medi- Proposed a model to help endocrinologist to choose thecations
best medicine

LABp with possible antidiabetic capabilitiesa

Sekar et al [39]Fuzzy PROMETHEEq

Choose the best course of therapy

Giving the high peace of treatment to the most affected
peopleMühlbacher et al[40]

AHP and BWSr

Evaluate patients’ preferences for various T2D treatment parameters

Proposed a model

Mahat and Ahmad [41]Fuzzy AHP

Identify and choose the most efficient
thermal massage treatment session

Number of therapy sessions (per day) was the most important factor

Pan et al [42]Fuzzy AHP

Determine the weights of the various
physiological factors

The mathematical model of exercise rehabilitation program for patients with diabetes was established

Rani et al [43]

COPRASs

Select T2D medication treatment

Developed a new formula-based PFSst and evaluatedits feasibility by applying the model on selecting theT2D pharmacological therapy

Balubaid andBasheikh [44]AHP

Developed a mathematical decisionMetformin, pioglitazone, sitagliptin, and glimepiride
making model that prioritizes the avail- were ranked first, second, third, and fourth, respectively
able diabetes medication based on criteria

Mühlbacher et al[45]

AHP and BWS

Examine the key patient-related decision For oral antidiabetes-treated patient groups and insulincriteria involved in the medicinal treat- treated patient groups, HbA1cu level, delay of insulin
ment of T2Dtherapy, and occurrence of hypoglycemia were ranked
first, second, and third, respectively

AHP: analytic hierarchy process.

b

T2D: type 2 diabetes.

c

SWARA: step-wise weigh assessment ratio analysis.

d

FMULTIMOORA: full multiplicative form.

e

WASPAS: Weighted Aggregated Sum Product Assessment.

f

CRITIC: Criteria Importance Through Intercriteria Correlation.

g

TOPSIS: technique for order of preference by similarity to ideal solution.

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CDSS: clinical decision support system.

i

AGI: α-glucosidase.

j

DDP4: dipeptidyl peptidase-4.

k

MET: meglitinide.

l

SU: sulfonylureas.

m

TZD: thiazolidinedione.

n

ANP: analytical network process.

o

MCDA: multicriteria decision analysis.

p

LAB: lactic acid bacteria.

q

PROMETHEE: preference ranking organization method for enrichment of evaluations.

r

BWS: best–worst-scaling.

s

COPRAS: Complex Proportional Assessment.

t

PFS: Pythagorean Fuzzy Set.

u

HbA1c: hemoglobin A1c.

Diagnosis of Diabetes
Table 2 displays that roughly 19% (12/63) of the publications
centered on the application of MCDM techniques for aiding

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general practitioners and endocrinologists in diagnosing
diabetes. Among these, AHP and TOPSIS were the mostcommonly cited methods, with 4 and 3 mentions, respectively.

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Table 2. Diabetes diagnosis publications.
Reference

Methods

Objective

Risk factors

Results

Zulqarnain et al [6]TOPSISa

Investigate the prevalence of diabetes among women and men

Age, weight, height, BMI, systolic

Female patients were more likely todevelop diabetes

and diastolic BPb, urine creatinine,
albuminuria, and ACRc

Abdulkareem et al
[7]

Fuzzy

Abbasi et al [46]AHP

Predict diabetes risks

Weakness, obesity, delayed healing, FAHPe model is an excellent tool foralopecia, muscle stiffness, polydipsia, diagnosing medical disorders based
polyuria, visual blurring, sudden
on many criteria
weight loss, and itching

Identify the most significant risk

A history of GDM or impaired glucose tolerance in previous pregnancies and a history of macrosomia in
the infant

AHPd

factors for GDM

a

f

N/Ag

Yas et al [47]Fuzzy
TOPSIS

Identify the symptoms of diabetes Age, pregnancies, glucose, blood
Proposed a framework to recognizepressure, skin thickness, insulin, BMI, the symptoms of disease
and diabetes pedigree function

Amin-Naseri and
Neshat [48]AHP

Determine the likelihood of devel- FBSi index, PRFj, BMI, diet, age, BP, DIBARk, a knowledge-based expert
oping T2Dh
gender, family history, and smoking systemstatus

El-Sappagh et al
[49]

Fuzzy
AHP

Diagnosis of diabetes

Baha et al [50]AHP

Diagnosis of diabetes

Heredity, sex, ethnicity, age, impaired Recognized top 3 most important risk
glucose tolerance, gestational diafactors: heredity, obesity, and physibetes, and so forthcal inactivity

Sharma and Sharma EDASm[51]

Forecast diabetes

N/A

Combined MCDMn with machinelearning techniques to find the best
forecasting model

Malapane et al [52]WPMo

Forecast diabetes

N/A

Combined WPM method with machine learning to select the best model

Felix et al [53]TOPSIS

Identification of the most important T2D risk factors in the Pima
Indian database

Blood glucose, BP, blood cholesterol, Blindness, obesity, and inactivity
obesity, blindness, physical inactivity were the risk factors with greatest
impactSankar and Jeyaraj
[54]

AHP

Forecast diabetes in women

N/A

Propose a model for predicting diabetes among women

Bondor andMureşan [55]TOPSIS

Solve the problem of multiN/A
collinearity between criteria in diabetes diagnosis

Proposed a new algorithm which removed the multicollinearity among
criteria

N/A

Created a new, systematically interpretable FRBSl framework

TOPSIS: technique for order of preference by similarity to ideal solution.

b

BP: blood pressure.

c

ACR: albumin creatinine ratio.

d

AHP: analytic hierarchy process.

e

FAHP: fuzzy analytic hierarchy process.

f

GDM: gestational diabetes mellitus.

g

N/A: not applicable.

hT2D: type 2 diabetes.

i

FBS: fasting blood sugar.

j

PRF: physical risk factors.

k

DIBAR: Created Diabetes Risk Assessment.

l

FRBS: fuzzy rule-based systems.

m

EDAS: evaluation based on distance for average solution.

n

MCDM: multicriteria decision-making.

o

WPM: Weighted Product Model.

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Meal Recommendation for Diabetes
According to Table 3, a total of 8 (13%) out of 63 publications
focused on using MCDM techniques to assist people with

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diabetes in making the healthiest food choices from their food
options, considering factors such as fat content, carbohydratecontent, and calorie count. Among these, AHP was mentioned
most frequently, with 6 instances.

Table 3. Meal recommendation publications.

aReference

Methods

Objective

Criteria

Gaikwad et al
[56]

AHPa

Recommend a particular ice cream forpatients with diabetes

Sugar, cholesterol, di- Ben & Jerry’s Butter Pecan was enriched with all
etary fiber, and proteins 4 criteria

Sharawat andDubey [57]AHP

Find out the best diet for a patient withdiabetes among 3 alternatives: solid
food, liquid food, and fluid foodCalories, body fat,healthy carbs, and dietary needs

Solid food was selected as the best

Santoso et al[58]

Fuzzy AHP

Designed a new yogurt product for patients with diabetes

N/Ab

N/A

Zadeh et al
[59]

AHP

Proposed a personalized meal-planning
strategy

N/A

Proposed an affordable and culturally appropriate
meals that would provide all the nutrition needed
for a diabetic while still being mindful of calories
and carbs

Gulint andKadam [60]AHP and

Gaikwad et al
[61]

ANPd

Recommendation of a particular ice
cream

Sugar, calories, choles- Selected a type of ice cream that satisfies all criteterol, and proteinsria

Gaikwad et al
[62]

AHP

Recommendation of a particular ice
cream

N/A

Gaikwad et al
[63]

AHP

Recommendation of a particular ice
cream

Sugar, protein, cholesterol, and dietary fiber

TOPSISc

Recommended shakes and ice cream for Sugar, cholesterol,patients with diabetes
carbs, fat, protein, anddietary fiber

Results

Selected a type of ice cream that satisfies all criteria

Proposed a model combination of AHP-GAe and
AHP-CIf to recommend an ice cream to patientswith diabetes
Patient having a high sugar level of 262 mg/dl canconsume an ice cream lower sugar like Breyers
butter almond, also patient with low sugar levelof 77 mg/dl can consume high sugar ice cream
like Breyers

AHP: analytic hierarchy process.

b

N/A: not applicable.

c

TOPSIS: technique for order of preference by similarity to ideal solution.

d

ANP: analytical network process.

e

AHP-CI: analytic hierarchy process–cohort intelligence.

f

AHP-GA: analytic hierarchy process–genetic algorithm.

Diabetes Management
Based on Table 4, additional applications of MCDM techniques,particularly AHP methods, in diabetes management (14/63,
22%) encompass tasks such as identifying ideal locations forhttps://medinform.jmir.org/2024/1/e47701

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diabetes clinics, allocating resources for diabetes care, assessing
the current diabetes applications, and constructing models toprioritize criteria that bolster the safety of the insulin supply
chain.

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Table 4. Diabetes management publications.

aReference

MethodResults

Gupta et al[64]

TOPSISa, VIKORb,
PROMETHEE II

Assess current mHealthd applications for T2De, including Glucose Buddy, mySugr, Diabetes: M, Blood
Glucose Tracker, and OneTouch Reveal

Wang et al[65]

ANPf and CRITICg

Assess the influence of social support on T2DMh self-management

Mishra et al
[66]

AHPi

Created and used the SCPj assessment methodology for Indian diabetes clinic

Mishra [67]AHP

Developed a customized service quality assessment model for diabetes care

Mishra [68]Fuzzy TOPSIS

Proposed 3 alternatives for the placement of a diabetes clinic using the SLPk method

Byun et al
[69]

AHP

Improving the treatment compliance of patients with diabetes

Mehrotra andKim [70]

New multicriterion, robust Calculate the amount of funding allocated to diabetes preventive initiatives across the United Statesweighted-sum methodology to reduce the weighted sum of diabetes prevalence and outcomes caused by improper health expenditure

Haji et al [71]

AHP and TOPSISc

Create a model that can prioritize and pick the optimal criterion for optimizing insulin safety

Suka et al [72] AHP

Described a clinical decision support system that enhance dynamic decision-making

Fico et al [73] AHP

Selected the best tool for screening and managing T2D

Long and Cen- AHP
tor [74]

Assess the relative significance of 4 frequently used diabetes quality indicators: measuring HbA1cl,

Gajdoš et al[75]

TOPSIS

Proposed a concept of chronic care management, which could increase effectiveness and reduce thecost of health care provided to patients with T2D

Gupta et al[76]

CODAS-FAHPn and MOO- Assess the usability of mHealth applications to monitor T2D by developing 2 hybrid decision-making
methods
RA-FAHPo

Chang et al[77]

Delphi-AHP

measuring LDLm, performing a dilated eye examination, and performing a foot examination

Recommended a Delphi-AHP framework to establish agreement in creating a decision-making algorithm
for evaluating the balance of benefits and risks associated with the use of complementary and alternative
medicine for diabetes

TOPSIS: technique for order of preference by similarity to ideal solution.

b

VIKOR: ViseKriterijumska Optimizacija I Kompromisno Resenje.

c

PROMETHEE II: preference ranking organization method for enrichment of evaluation II.

dmHealth: mobile health.

e

T2D: type 2 diabetes.

f

ANP: analytical network process.

g

CRITIC: Criteria Importance Through Intercriteria CorrelationhT2DM: type 2 diabetes mellitus.

i

AHP: analytic hierarchy process.

j

SCP: Supply Chain Partnership.

k

SLP: Systematic Layout Planning.

l

HbA1c: hemoglobin A1c.

m

LDL: low-density lipoprotein.

n

CODAS-FAHP: combine distance-based assessment-fuzzy AHP.

o

MOORA-FAHP: multiobjective optimization on the basis of ratio analysis-fuzzy AHP.

Diabetes Complication
T2D is a significant global public health issue, characterizedby 2 categories of harm: macrovascular (involving large arteries)
and microvascular (involving small blood vessels).
Macrovascular disease such as strokes and microvascular

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diseases such as retinopathy, nephropathy, and neuropathy [7].
MCDM techniques, especially TOPSIS, as shown in Table 5,
are used to assist endocrinologists and general practitioners inanalyzing the severity of these complications, forecasting theirlikelihood of occurrence, and pinpointing the risk factors forthem (n=7).

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Aldaghi & Muzik

Table 5. Diabetes complication diagnosis publications.
Reference

Methods

Objective

Ebrahimi and
Ahmadi [78]Fuzzy TOP-

Analyzed the severity High cholesterol, high BPb,
caused by diabetes
obesity, physical inactivity,
smoking, family history, age,and sex

Neuropathy, diabetic
Cardiovascular disease wasretinopathy, cardiovascu- the most important complicalar disease, kidney distion in the problem
ease, foot ulcer, and amputation

Ahmadi andEbrahimi [79]MCDMc

Assessed the severityof difficulties causedby diabetes

Ischemic heart disease, heart
failure, heart stroke, ketoacidosis, diabetic ulcer, neuropathy,
and lower extremely amputation

Cardiovascular disease,diabetic ketoacidosis,
lower extremity complications, and lower extremity amputation

Bondor et al[80]

TOPSIS

Identification of therisk factors in kidneydiseaseUrinary albumin per creatinine Diabetic kidney
ratio and glomerular filtration

SISa

Criteria

Complications

Results

Proposed a new hybrid algorithm that calculate the severity of damage caused by diabetes
Rank the risk factors of microalbuminuria and eGFRd toevaluate the risk factor for
CKDe

Ahmed et al[81]

Bondor et al[82]

TOPSIS andentropy

VIKORlDetection of DRf
through machinelearning and TOPSIS
modelsCriteria of TOPSIS model:

Rank risk factors ofdiabetic kidney disease

Serum adiponectin, triglycerides, SBP, duration of diabetes and age, Malondialde-

DR

According to TOPSIS, Adaboost model ranks at the bestmodel to detect DR

Diabetic kidney

Identification of diabetic kidney disease risk factors

The model showed the applicability and impact of mental
health in patients with diabetes

AUCg, accuracy, precision, F1score, recall, TPRh, FNRi,
FPRj, TNRk, and timehyde, and HDLm-cholesterola

Alassery et al[83]

Fuzzy AHPn
and Fuzzy
TOPSIS

Determine the impact BMI, SBP, DBPo, age, height,of mental health inexercisepatients with diabetes

Mental health

Wang et al [84]

AHP

Relieve the pain in
N/Appatients with diabetes

Diabetic neuropathy and Selection of shoe lasts forfoot ulcers
footwear design to help relieve the pain associated withdiabetic neuropathy and footulcersTOPSIS: technique for order of preference by similarity to ideal solution.

b

BP: blood pressure.

c

MCDM: multicriteria decision-making.

d

GFR: estimated glomerular filtration rate.

e

CKD: chronic kidney disease.

f

DR: diabetic retinopathy.

g

AUC: area under the curve.

hTPR: true positive rate.

i

FNR: false negative rate.

j

FPR: false positive rate.

k

TNR: true negative rate.

l

VIKOR: ViseKriterijumska Optimizacija I Kompromisno Resenje.

m

HDL: high-density lipoprotein.

n

AHP: analytic hierarchy process.

o

DBP: diastolic blood pressure.

p

N/A: not applicable.

Discussion
Principal Findings
Given the multitude of choices involved in selecting diabetes
medication, meal planning, nutrient intake, diabetes management
apps, and speedy diagnosis, endocrinologists, general
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practitioners, and individuals with diabetes, along with theircaregivers, need guidance to make informed decisions. MCDM
is a quantitative approach that effectively integrates treatment
benefits and drawbacks, as well as individual preferences, tofacilitate sound medical decision-making in these complex
situations. Consequently, we embarked on an evaluation of theeffectiveness of MCDM methods in the context of diabetes.
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JMIR MEDICAL INFORMATICS
Based on a notable upward trend in publications within therealm of using MCDM methods in diabetes research over thelast 2 decades, this underscores the growing interest among
researchers in applying MCDM methods to address
diabetes-related challenges. Furthermore, the majority of thesepublications (n=19) focus on diabetes treatment selection[14,28-45]. Diabetes management (n=14), diagnosis of diabetes(n=12), meal recommendation (n=8), diabetes complications(n=7), and global estimation (n=3) are in the later ranks. Thisoutcome highlights the efficacy of using MCDM methods inthe process of choosing diabetes medications.
All MCDM methods in diabetes are classified into 13 groups.
AHP is ranked first, having been used in 25 articles. AHP isdesigned to help individuals and groups make complex decisions
by breaking them into a hierarchical structure, comparing andweighting criteria and alternatives, and deriving a rational choicebased on these comparisons [7,85,24]. AHP can be applied todiabetes issues and decision-making in several ways including
treatment selection [14,31,32,34,36,38-42,44,45], diabetes
diagnosis [46,48-50,54], dietary planning [56-60,62,63], diabetes
management [66,67,69,71-74,77], complication diagnosis [84],and estimating diabetes prevalence [4,5]. TOPSIS and fuzzy
AHP with 9 and 8 publications are in the next ranks,
respectively.
As observed, 6 distinct weighting algorithms were recognized,with the Entropy approach ranking highest. The final component
in our proposed classification pertains to estimating diabetes
prevalence. In a 2013 study, researchers used logistic regression
and AHP techniques to produce smoothed age-specificoccurrence estimates for adults aged 20 to 79 years. Theseestimates were then used to calculate population projections forthe years 2013 and 2035, foreseeing an increase in the numberof individuals with diabetes to 592 million by 2035 [4]. In
another investigation conducted by the IDF in 2015, AHP andlogistic regression methods were used to estimate that there
were 415 million people (ranging from 340 million to 536
million) with diabetes. Projections indicate that this figure is

Aldaghi & Muzik
expected to reach 642 million (ranging from 521 million to 829
million) by 2040 [5].

Conclusions
One of the most serious health problems of the 21st century,
whose prevalence is rapidly increasing, is diabetes mellitus.
Almost all areas of diabetes research have seen significant
progress to date, particularly in the areas of medication selection,
meal selection, diabetes management applications, use ofcontinuous glucose monitoring, and closed-loop system. The
advancement of technology has expanded the scope ofdecision-making responsibilities for general practitioners in theinitial stages of patient care. Determining the most optimal
choice among numerous options falls within the domain of
MCDM.
In this research, for the first time, we reviewed the majority of
MCDM papers for diabetes and considered 2 important issues
in the field of diabetes: examining the usability of MCDM
techniques in diabetes and proposing a new classification of
applications of MCDM methods in diabetes. Our study
highlights that the use of MCDM techniques extends beyond
the realm of diabetes medication selection. These methods holdpromise for diverse applications, spanning meal planning,diabetes diagnosis, and addressing diabetes-related challenges.
This includes tasks such as selecting optimal diabetes
management applications from a wide range of options,
identifying ideal locations for diabetes clinics, and efficiently
allocating resources for diabetes care. Moreover, the analysis
reveals that AHP is the preferred and widely embraced strategy
and approach, primarily owing to its straightforward structure
and user-friendliness. We firmly believe that the adoption of
MCDM approaches offers advantages to a broad spectrum of
stakeholders, including patients with diabetes, endocrinologists,
general practitioners, caregivers, and health care policy makers.
These techniques have the potential to serve as valuable tools
for general practitioners, assisting in quicker diabetes diagnosis
and more accurate medication selection, ultimately reducing
patient costs and lifestyle concerns.

Acknowledgments
This research was supported by the project TN02000067—Future Electronics for Industry 4.0 and Medical 4.0 is cofinanced
from the state budget by the Technology Agency of the Czech Republic under the National Centers of Competence: support
programme for applied research, experimental development, and innovation.

Conflicts of Interest
None declared.

Multimedia Appendix 1
PRISMA checklist.
[DOCX File , 35 KB-Multimedia Appendix 1]

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Abbreviations
ADA: American Diabetes Association
AHP: analytic hierarchy process
ANP: analytical network process
CRITIC: Criteria Importance Through Intercriteria Correlation
ELECTRE: Elimination Et Choix Traduisant la Realité
IDF: International Diabetes Federation
MADM: multiattribute decision-making
MCDA: multicriteria decision-analysis
MCDM: multicriteria decision-making
MeSH: Medical Subject Headings
MODM: multiobjective decision-making
PRISMA: Preferred Reporting Items for Systematic Review and Meta-Analyses
PROMETHEE: preference ranking organization method for enrichment of evaluations
SWARA: step-wise weigh assessment ratio analysis
TOPSIS: technique for order of preference by similarity to ideal solution
T2D: type 2 diabetes
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Aldaghi & Muzik

VIKOR: ViseKriterijumska Optimizacija I Kompromisno Resenje
WASPAS: Weighted Aggregated Sum Product Assessment
WPM: Weighted Product Model
WSM: Weighted Sum Model

Edited by A Castonguay; submitted 29.03.23; peer-reviewed by E Nazarie, J Sussman, A Kandwal, A Ranusch; comments to author
31.08.23; revised version received 24.10.23; accepted 11.12.23; published 01.02.24
Please cite as:
Aldaghi T, Muzik J
Multicriteria Decision-Making in Diabetes Management and Decision Support: Systematic Review
JMIR Med Inform 2024;12:e47701
URL: https://medinform.jmir.org/2024/1/e47701
doi: 10.2196/47701
PMID: 38300703©Tahmineh Aldaghi, Jan Muzik. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 01.02.2024.
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